REF: make ccalendar objects cimportable (#34106)

Author: jbrockmendel

pandas/_libs/tslibs/ccalendar.pxd

@@ -10,3 +10,7 @@ cpdef int32_t get_days_in_month(int year, Py_ssize_t month) nogil
 cpdef int32_t get_week_of_year(int year, int month, int day) nogil
 cpdef iso_calendar_t get_iso_calendar(int year, int month, int day) nogil
 cpdef int32_t get_day_of_year(int year, int month, int day) nogil
+
+cdef int64_t DAY_NANOS
+cdef int64_t HOUR_NANOS
+cdef dict c_MONTH_NUMBERS

pandas/_libs/tslibs/ccalendar.pyx

@@ -40,6 +40,7 @@ MONTHS_FULL = ['', 'January', 'February', 'March', 'April', 'May', 'June',
                'July', 'August', 'September', 'October', 'November',
                'December']
 MONTH_NUMBERS = {name: num for num, name in enumerate(MONTHS)}
+cdef dict c_MONTH_NUMBERS = MONTH_NUMBERS
 MONTH_ALIASES = {(num + 1): name for num, name in enumerate(MONTHS)}
 MONTH_TO_CAL_NUM = {name: num + 1 for num, name in enumerate(MONTHS)}
 
@@ -52,6 +53,9 @@ weekday_to_int = {int_to_weekday[key]: key for key in int_to_weekday}
 DAY_SECONDS = 86400
 HOUR_SECONDS = 3600
 
+cdef int64_t DAY_NANOS = DAY_SECONDS * 1_000_000_000
+cdef int64_t HOUR_NANOS = HOUR_SECONDS * 1_000_000_000
+
 # ----------------------------------------------------------------------
 
 

pandas/_libs/tslibs/fields.pyx

@@ -12,8 +12,10 @@ from numpy cimport ndarray, int64_t, int32_t, int8_t, uint32_t
 cnp.import_array()
 
 from pandas._libs.tslibs.ccalendar import (
-    get_locale_names, MONTHS_FULL, DAYS_FULL, DAY_SECONDS)
+    get_locale_names, MONTHS_FULL, DAYS_FULL,
+)
 from pandas._libs.tslibs.ccalendar cimport (
+    DAY_NANOS,
     get_days_in_month, is_leapyear, dayofweek, get_week_of_year,
     get_day_of_year, get_iso_calendar, iso_calendar_t)
 from pandas._libs.tslibs.np_datetime cimport (
@@ -38,7 +40,7 @@ def get_time_micros(const int64_t[:] dtindex):
     cdef:
         ndarray[int64_t] micros
 
-    micros = np.mod(dtindex, DAY_SECONDS * 1_000_000_000, dtype=np.int64)
+    micros = np.mod(dtindex, DAY_NANOS, dtype=np.int64)
     micros //= 1000
     return micros
 

pandas/_libs/tslibs/frequencies.pyx

@@ -5,7 +5,7 @@ cnp.import_array()
 
 from pandas._libs.tslibs.util cimport is_integer_object, is_offset_object
 
-from pandas._libs.tslibs.ccalendar import MONTH_NUMBERS
+from pandas._libs.tslibs.ccalendar cimport c_MONTH_NUMBERS
 
 # ----------------------------------------------------------------------
 # Constants
@@ -458,8 +458,8 @@ cdef str _maybe_coerce_freq(code):
 
 
 cdef bint _quarter_months_conform(str source, str target):
-    snum = MONTH_NUMBERS[source]
-    tnum = MONTH_NUMBERS[target]
+    snum = c_MONTH_NUMBERS[source]
+    tnum = c_MONTH_NUMBERS[target]
     return snum % 3 == tnum % 3
 
 

pandas/_libs/tslibs/parsing.pyx

@@ -32,7 +32,7 @@ from dateutil.parser import parse as du_parse
 
 from pandas._config import get_option
 
-from pandas._libs.tslibs.ccalendar import MONTH_NUMBERS
+from pandas._libs.tslibs.ccalendar cimport c_MONTH_NUMBERS
 from pandas._libs.tslibs.nattype import nat_strings, NaT
 from pandas._libs.tslibs.util cimport (
     is_array,
@@ -435,9 +435,9 @@ cdef inline object _parse_dateabbr_string(object date_string, object default,
                                      f'between 1 and 4: {date_string}')
 
             if freq is not None:
-                # hack attack, #1228
+                # TODO: hack attack, #1228
                 try:
-                    mnum = MONTH_NUMBERS[get_rule_month(freq)] + 1
+                    mnum = c_MONTH_NUMBERS[get_rule_month(freq)] + 1
                 except (KeyError, ValueError):
                     raise DateParseError(f'Unable to retrieve month '
                                          f'information from given '

pandas/_libs/tslibs/period.pyx

@@ -44,9 +44,14 @@ from pandas._libs.tslibs.timezones cimport is_utc, is_tzlocal, get_dst_info
 from pandas._libs.tslibs.timedeltas import Timedelta
 from pandas._libs.tslibs.timedeltas cimport delta_to_nanoseconds
 
-cimport pandas._libs.tslibs.ccalendar as ccalendar
-from pandas._libs.tslibs.ccalendar cimport dayofweek, get_day_of_year, is_leapyear
-from pandas._libs.tslibs.ccalendar import MONTH_NUMBERS
+from pandas._libs.tslibs.ccalendar cimport (
+    dayofweek,
+    get_day_of_year,
+    is_leapyear,
+    get_week_of_year,
+    get_days_in_month,
+)
+from pandas._libs.tslibs.ccalendar cimport c_MONTH_NUMBERS
 from pandas._libs.tslibs.frequencies cimport (
     get_base_alias,
     get_freq_code,
@@ -1315,7 +1320,7 @@ cdef int pweek(int64_t ordinal, int freq):
     cdef:
         npy_datetimestruct dts
     get_date_info(ordinal, freq, &dts)
-    return ccalendar.get_week_of_year(dts.year, dts.month, dts.day)
+    return get_week_of_year(dts.year, dts.month, dts.day)
 
 
 cdef int phour(int64_t ordinal, int freq):
@@ -1343,7 +1348,7 @@ cdef int pdays_in_month(int64_t ordinal, int freq):
     cdef:
         npy_datetimestruct dts
     get_date_info(ordinal, freq, &dts)
-    return ccalendar.get_days_in_month(dts.year, dts.month)
+    return get_days_in_month(dts.year, dts.month)
 
 
 @cython.wraparound(False)
@@ -2486,7 +2491,7 @@ def quarter_to_myear(year: int, quarter: int, freq):
     if quarter <= 0 or quarter > 4:
         raise ValueError('Quarter must be 1 <= q <= 4')
 
-    mnum = MONTH_NUMBERS[get_rule_month(freq)] + 1
+    mnum = c_MONTH_NUMBERS[get_rule_month(freq)] + 1
     month = (mnum + (quarter - 1) * 3) % 12 + 1
     if month > mnum:
         year -= 1

pandas/_libs/tslibs/timedeltas.pyx

@@ -23,7 +23,7 @@ from pandas._libs.tslibs.util cimport (
 
 from pandas._libs.tslibs.base cimport ABCTimedelta, ABCTimestamp, is_tick_object
 
-from pandas._libs.tslibs.ccalendar import DAY_SECONDS
+from pandas._libs.tslibs.ccalendar cimport DAY_NANOS
 
 from pandas._libs.tslibs.np_datetime cimport (
     cmp_scalar, reverse_ops, td64_to_tdstruct, pandas_timedeltastruct)
@@ -277,10 +277,10 @@ cpdef inline object precision_from_unit(str unit):
         m = 1000000000 * 2629746
         p = 9
     elif unit == 'W':
-        m = 1000000000 * DAY_SECONDS * 7
+        m = DAY_NANOS * 7
         p = 9
     elif unit == 'D' or unit == 'd':
-        m = 1000000000 * DAY_SECONDS
+        m = DAY_NANOS
         p = 9
     elif unit == 'h':
         m = 1000000000 * 3600

pandas/_libs/tslibs/timestamps.pyx

@@ -27,8 +27,8 @@ from pandas._libs.tslibs.util cimport (
 
 from pandas._libs.tslibs.base cimport ABCTimestamp, is_tick_object
 
-cimport pandas._libs.tslibs.ccalendar as ccalendar
-from pandas._libs.tslibs.ccalendar import DAY_SECONDS
+from pandas._libs.tslibs cimport ccalendar
+
 from pandas._libs.tslibs.conversion import normalize_i8_timestamps
 from pandas._libs.tslibs.conversion cimport (
     _TSObject, convert_to_tsobject,
@@ -1504,7 +1504,7 @@ default 'raise'
         Normalize Timestamp to midnight, preserving tz information.
         """
         if self.tz is None or is_utc(self.tz):
-            DAY_NS = DAY_SECONDS * 1_000_000_000
+            DAY_NS = ccalendar.DAY_NANOS
             normalized_value = self.value - (self.value % DAY_NS)
             return Timestamp(normalized_value).tz_localize(self.tz)
         normalized_value = normalize_i8_timestamps(

pandas/_libs/tslibs/tzconversion.pyx

@@ -16,7 +16,7 @@ cimport numpy as cnp
 from numpy cimport ndarray, int64_t, uint8_t, intp_t
 cnp.import_array()
 
-from pandas._libs.tslibs.ccalendar import DAY_SECONDS, HOUR_SECONDS
+from pandas._libs.tslibs.ccalendar cimport DAY_NANOS, HOUR_NANOS
 from pandas._libs.tslibs.nattype cimport NPY_NAT
 from pandas._libs.tslibs.np_datetime cimport (
     npy_datetimestruct, dt64_to_dtstruct)
@@ -71,7 +71,7 @@ timedelta-like}
         int64_t *tdata
         int64_t v, left, right, val, v_left, v_right, new_local, remaining_mins
         int64_t first_delta
-        int64_t HOURS_NS = HOUR_SECONDS * 1000000000, shift_delta = 0
+        int64_t shift_delta = 0
         ndarray[int64_t] trans, result, result_a, result_b, dst_hours, delta
         ndarray trans_idx, grp, a_idx, b_idx, one_diff
         npy_datetimestruct dts
@@ -142,10 +142,10 @@ timedelta-like}
     result_b[:] = NPY_NAT
 
     idx_shifted_left = (np.maximum(0, trans.searchsorted(
-        vals - DAY_SECONDS * 1000000000, side='right') - 1)).astype(np.int64)
+        vals - DAY_NANOS, side='right') - 1)).astype(np.int64)
 
     idx_shifted_right = (np.maximum(0, trans.searchsorted(
-        vals + DAY_SECONDS * 1000000000, side='right') - 1)).astype(np.int64)
+        vals + DAY_NANOS, side='right') - 1)).astype(np.int64)
 
     for i in range(n):
         val = vals[i]
@@ -240,18 +240,18 @@ timedelta-like}
             # Handle nonexistent times
             if shift_forward or shift_backward or shift_delta != 0:
                 # Shift the nonexistent time to the closest existing time
-                remaining_mins = val % HOURS_NS
+                remaining_mins = val % HOUR_NANOS
                 if shift_delta != 0:
                     # Validate that we don't relocalize on another nonexistent
                     # time
-                    if -1 < shift_delta + remaining_mins < HOURS_NS:
+                    if -1 < shift_delta + remaining_mins < HOUR_NANOS:
                         raise ValueError(
                             f"The provided timedelta will relocalize on a "
                             f"nonexistent time: {nonexistent}"
                         )
                     new_local = val + shift_delta
                 elif shift_forward:
-                    new_local = val + (HOURS_NS - remaining_mins)
+                    new_local = val + (HOUR_NANOS - remaining_mins)
                 else:
                     # Subtract 1 since the beginning hour is _inclusive_ of
                     # nonexistent times