REF: use standard patterns in tslibs (#46259)

* REF: use standard patterns in tslibs

* lint fixup

* avoid not-initialized-warning

Author: jbrockmendel

pandas/_libs/tslibs/conversion.pxd

@@ -12,10 +12,10 @@ from pandas._libs.tslibs.np_datetime cimport npy_datetimestruct
 
 
 cdef class _TSObject:
-    cdef:
+    cdef readonly:
         npy_datetimestruct dts      # npy_datetimestruct
         int64_t value               # numpy dt64
-        object tzinfo
+        tzinfo tzinfo
         bint fold
 
 

pandas/_libs/tslibs/conversion.pyx

@@ -370,18 +370,13 @@ cdef class _TSObject:
     # cdef:
     #    npy_datetimestruct dts      # npy_datetimestruct
     #    int64_t value               # numpy dt64
-    #    object tzinfo
+    #    tzinfo tzinfo
     #    bint fold
 
     def __cinit__(self):
         # GH 25057. As per PEP 495, set fold to 0 by default
         self.fold = 0
 
-    @property
-    def value(self):
-        # This is needed in order for `value` to be accessible in lib.pyx
-        return self.value
-
 
 cdef convert_to_tsobject(object ts, tzinfo tz, str unit,
                          bint dayfirst, bint yearfirst, int32_t nanos=0):
@@ -541,7 +536,7 @@ cdef _TSObject _create_tsobject_tz_using_offset(npy_datetimestruct dts,
         int64_t value  # numpy dt64
         datetime dt
         ndarray[int64_t] trans
-        int64_t[:] deltas
+        int64_t[::1] deltas
 
     value = dtstruct_to_dt64(&dts)
     obj.dts = dts
@@ -711,7 +706,7 @@ cdef inline void _localize_tso(_TSObject obj, tzinfo tz):
     """
     cdef:
         ndarray[int64_t] trans
-        int64_t[:] deltas
+        int64_t[::1] deltas
         int64_t local_val
         Py_ssize_t pos
         str typ
@@ -729,26 +724,27 @@ cdef inline void _localize_tso(_TSObject obj, tzinfo tz):
         # Adjust datetime64 timestamp, recompute datetimestruct
         trans, deltas, typ = get_dst_info(tz)
 
-        if is_fixed_offset(tz):
-            # static/fixed tzinfo; in this case we know len(deltas) == 1
-            # This can come back with `typ` of either "fixed" or None
-            dt64_to_dtstruct(obj.value + deltas[0], &obj.dts)
-        elif typ == 'pytz':
+        if typ == "pytz":
             # i.e. treat_tz_as_pytz(tz)
-            pos = trans.searchsorted(obj.value, side='right') - 1
+            pos = trans.searchsorted(obj.value, side="right") - 1
+            local_val = obj.value + deltas[pos]
+
+            # find right representation of dst etc in pytz timezone
             tz = tz._tzinfos[tz._transition_info[pos]]
-            dt64_to_dtstruct(obj.value + deltas[pos], &obj.dts)
-        elif typ == 'dateutil':
+        elif typ == "dateutil":
             # i.e. treat_tz_as_dateutil(tz)
-            pos = trans.searchsorted(obj.value, side='right') - 1
-            dt64_to_dtstruct(obj.value + deltas[pos], &obj.dts)
+            pos = trans.searchsorted(obj.value, side="right") - 1
+            local_val = obj.value + deltas[pos]
+
             # dateutil supports fold, so we infer fold from value
             obj.fold = _infer_tsobject_fold(obj, trans, deltas, pos)
         else:
-            # Note: as of 2018-07-17 all tzinfo objects that are _not_
-            # either pytz or dateutil have is_fixed_offset(tz) == True,
-            # so this branch will never be reached.
-            pass
+            # All other cases have len(deltas) == 1. As of 2018-07-17
+            #  (and 2022-03-07), all test cases that get here have
+            #  is_fixed_offset(tz).
+            local_val = obj.value + deltas[0]
+
+        dt64_to_dtstruct(local_val, &obj.dts)
 
     obj.tzinfo = tz
 

pandas/_libs/tslibs/tzconversion.pyx

@@ -52,7 +52,7 @@ cdef int64_t tz_localize_to_utc_single(
     """See tz_localize_to_utc.__doc__"""
     cdef:
         int64_t delta
-        int64_t[:] deltas
+        int64_t[::1] deltas
 
     if val == NPY_NAT:
         return val
@@ -115,9 +115,10 @@ timedelta-like}
     localized : ndarray[int64_t]
     """
     cdef:
-        int64_t[:] deltas, idx_shifted, idx_shifted_left, idx_shifted_right
+        int64_t[::1] deltas
+        int64_t[:] idx_shifted, idx_shifted_left, idx_shifted_right
         ndarray[uint8_t, cast=True] ambiguous_array, both_nat, both_eq
-        Py_ssize_t i, idx, pos, ntrans, n = len(vals)
+        Py_ssize_t i, idx, pos, ntrans, n = vals.shape[0]
         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
@@ -184,7 +185,7 @@ timedelta-like}
     trans, deltas, _ = get_dst_info(tz)
 
     tdata = <int64_t*>cnp.PyArray_DATA(trans)
-    ntrans = len(trans)
+    ntrans = trans.shape[0]
 
     # Determine whether each date lies left of the DST transition (store in
     # result_a) or right of the DST transition (store in result_b)
@@ -400,7 +401,7 @@ cpdef int64_t tz_convert_from_utc_single(int64_t val, tzinfo tz):
     """
     cdef:
         int64_t delta
-        int64_t[:] deltas
+        int64_t[::1] deltas
         ndarray[int64_t, ndim=1] trans
         intp_t pos
 
@@ -437,11 +438,11 @@ def tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
     cdef:
         const int64_t[:] converted
 
-    if len(vals) == 0:
+    if vals.shape[0] == 0:
         return np.array([], dtype=np.int64)
 
     converted = _tz_convert_from_utc(vals, tz)
-    return np.array(converted, dtype=np.int64)
+    return np.asarray(converted, dtype=np.int64)
 
 
 @cython.boundscheck(False)
@@ -460,53 +461,48 @@ cdef const int64_t[:] _tz_convert_from_utc(const int64_t[:] vals, tzinfo tz):
     converted : ndarray[int64_t]
     """
     cdef:
-        int64_t[:] converted, deltas
-        Py_ssize_t i, n = len(vals)
-        int64_t val, delta
+        int64_t[::1] converted, deltas
+        Py_ssize_t i, n = vals.shape[0]
+        int64_t val, delta = 0  # avoid not-initialized-warning
         intp_t[:] pos
         ndarray[int64_t] trans
         str typ
+        bint use_tzlocal = False, use_fixed = False, use_utc = True
 
     if is_utc(tz):
-        return vals
+        # Much faster than going through the "standard" pattern below
+        return vals.copy()
+
+    if is_utc(tz) or tz is None:
+        use_utc = True
     elif is_tzlocal(tz):
-        converted = np.empty(n, dtype=np.int64)
-        for i in range(n):
-            val = vals[i]
-            if val == NPY_NAT:
-                converted[i] = NPY_NAT
-            else:
-                converted[i] = _tz_convert_tzlocal_utc(val, tz, to_utc=False)
+        use_tzlocal = True
     else:
-        converted = np.empty(n, dtype=np.int64)
-
         trans, deltas, typ = get_dst_info(tz)
 
         if typ not in ["pytz", "dateutil"]:
             # FixedOffset, we know len(deltas) == 1
             delta = deltas[0]
-
-            for i in range(n):
-                val = vals[i]
-                if val == NPY_NAT:
-                    converted[i] = val
-                else:
-                    converted[i] = val + delta
-
+            use_fixed = True
         else:
             pos = trans.searchsorted(vals, side="right") - 1
 
-            for i in range(n):
-                val = vals[i]
-                if val == NPY_NAT:
-                    converted[i] = val
-                else:
-                    if pos[i] < 0:
-                        # TODO: How is this reached?  Should we be checking for
-                        #  it elsewhere?
-                        raise ValueError("First time before start of DST info")
+    converted = np.empty(n, dtype=np.int64)
 
-                    converted[i] = val + deltas[pos[i]]
+    for i in range(n):
+        val = vals[i]
+        if val == NPY_NAT:
+            converted[i] = NPY_NAT
+            continue
+
+        # The pattern used in vectorized.pyx checks for use_utc here,
+        #  but we handle that case above.
+        if use_tzlocal:
+            converted[i] = _tz_convert_tzlocal_utc(val, tz, to_utc=False)
+        elif use_fixed:
+            converted[i] = val + delta
+        else:
+            converted[i] = val + deltas[pos[i]]
 
     return converted
 
@@ -547,8 +543,10 @@ cdef int64_t _tz_convert_tzlocal_utc(int64_t val, tzinfo tz, bint to_utc=True,
         timedelta td
 
     dt64_to_dtstruct(val, &dts)
+
     dt = datetime(dts.year, dts.month, dts.day, dts.hour,
                   dts.min, dts.sec, dts.us)
+
     # tz.utcoffset only makes sense if datetime
     # is _wall time_, so if val is a UTC timestamp convert to wall time
     if not to_utc:

pandas/_libs/tslibs/vectorized.pyx

@@ -121,7 +121,7 @@ def ints_to_pydatetime(
     cdef:
         Py_ssize_t i, n = len(stamps)
         ndarray[int64_t] trans
-        int64_t[:] deltas
+        int64_t[::1] deltas
         intp_t[:] pos
         npy_datetimestruct dts
         object dt, new_tz
@@ -167,26 +167,23 @@ def ints_to_pydatetime(
 
         if value == NPY_NAT:
             result[i] = <object>NaT
+            continue
+
+        if use_utc:
+            local_val = value
+        elif use_tzlocal:
+            local_val = tz_convert_utc_to_tzlocal(value, tz)
+        elif use_fixed:
+            local_val = value + delta
         else:
-            if use_utc:
-                local_val = value
-            elif use_tzlocal:
-                local_val = tz_convert_utc_to_tzlocal(value, tz)
-            elif use_fixed:
-                local_val = value + delta
-            elif not use_pytz:
-                # i.e. dateutil
-                # no zone-name change for dateutil tzs - dst etc
-                # represented in single object.
-                local_val = value + deltas[pos[i]]
-            else:
-                # pytz
-                # find right representation of dst etc in pytz timezone
-                new_tz = tz._tzinfos[tz._transition_info[pos[i]]]
-                local_val = value + deltas[pos[i]]
-
-            dt64_to_dtstruct(local_val, &dts)
-            result[i] = func_create(value, dts, new_tz, freq, fold)
+            local_val = value + deltas[pos[i]]
+
+        if use_pytz:
+            # find right representation of dst etc in pytz timezone
+            new_tz = tz._tzinfos[tz._transition_info[pos[i]]]
+
+        dt64_to_dtstruct(local_val, &dts)
+        result[i] = func_create(value, dts, new_tz, freq, fold)
 
     return result
 
@@ -226,7 +223,7 @@ def get_resolution(const int64_t[:] stamps, tzinfo tz=None) -> Resolution:
         npy_datetimestruct dts
         int reso = RESO_DAY, curr_reso
         ndarray[int64_t] trans
-        int64_t[:] deltas
+        int64_t[::1] deltas
         intp_t[:] pos
         int64_t local_val, delta = NPY_NAT
         bint use_utc = False, use_tzlocal = False, use_fixed = False
@@ -288,7 +285,7 @@ cpdef ndarray[int64_t] normalize_i8_timestamps(const int64_t[:] stamps, tzinfo t
         Py_ssize_t i, n = len(stamps)
         int64_t[:] result = np.empty(n, dtype=np.int64)
         ndarray[int64_t] trans
-        int64_t[:] deltas
+        int64_t[::1] deltas
         str typ
         Py_ssize_t[:] pos
         int64_t local_val, delta = NPY_NAT
@@ -346,7 +343,7 @@ def is_date_array_normalized(const int64_t[:] stamps, tzinfo tz=None) -> bool:
     cdef:
         Py_ssize_t i, n = len(stamps)
         ndarray[int64_t] trans
-        int64_t[:] deltas
+        int64_t[::1] deltas
         intp_t[:] pos
         int64_t local_val, delta = NPY_NAT
         str typ
@@ -392,7 +389,7 @@ def dt64arr_to_periodarr(const int64_t[:] stamps, int freq, tzinfo tz):
         Py_ssize_t i, n = len(stamps)
         int64_t[:] result = np.empty(n, dtype=np.int64)
         ndarray[int64_t] trans
-        int64_t[:] deltas
+        int64_t[::1] deltas
         Py_ssize_t[:] pos
         npy_datetimestruct dts
         int64_t local_val, delta = NPY_NAT