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818 lines
22 KiB
818 lines
22 KiB
"""
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Functions for accessing attributes of Timestamp/datetime64/datetime-like
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objects and arrays
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"""
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from locale import LC_TIME
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from _strptime import LocaleTime
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cimport cython
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from cython cimport Py_ssize_t
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import numpy as np
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cimport numpy as cnp
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from numpy cimport (
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int8_t,
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int32_t,
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int64_t,
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ndarray,
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uint32_t,
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)
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cnp.import_array()
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from pandas._config.localization import set_locale
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from pandas._libs.tslibs.ccalendar import (
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DAYS_FULL,
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MONTHS_FULL,
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)
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from pandas._libs.tslibs.ccalendar cimport (
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dayofweek,
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get_day_of_year,
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get_days_in_month,
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get_firstbday,
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get_iso_calendar,
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get_lastbday,
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get_week_of_year,
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iso_calendar_t,
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)
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from pandas._libs.tslibs.nattype cimport NPY_NAT
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from pandas._libs.tslibs.np_datetime cimport (
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NPY_DATETIMEUNIT,
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NPY_FR_ns,
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import_pandas_datetime,
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npy_datetimestruct,
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pandas_datetime_to_datetimestruct,
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pandas_timedelta_to_timedeltastruct,
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pandas_timedeltastruct,
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)
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import_pandas_datetime()
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def build_field_sarray(const int64_t[:] dtindex, NPY_DATETIMEUNIT reso):
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"""
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Datetime as int64 representation to a structured array of fields
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"""
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cdef:
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Py_ssize_t i, count = len(dtindex)
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npy_datetimestruct dts
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ndarray[int32_t] years, months, days, hours, minutes, seconds, mus
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sa_dtype = [
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("Y", "i4"), # year
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("M", "i4"), # month
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("D", "i4"), # day
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("h", "i4"), # hour
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("m", "i4"), # min
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("s", "i4"), # second
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("u", "i4"), # microsecond
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]
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out = np.empty(count, dtype=sa_dtype)
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years = out["Y"]
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months = out["M"]
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days = out["D"]
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hours = out["h"]
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minutes = out["m"]
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seconds = out["s"]
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mus = out["u"]
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for i in range(count):
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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years[i] = dts.year
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months[i] = dts.month
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days[i] = dts.day
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hours[i] = dts.hour
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minutes[i] = dts.min
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seconds[i] = dts.sec
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mus[i] = dts.us
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return out
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def month_position_check(fields, weekdays) -> str | None:
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cdef:
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int32_t daysinmonth, y, m, d
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bint calendar_end = True
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bint business_end = True
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bint calendar_start = True
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bint business_start = True
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bint cal
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int32_t[:] years = fields["Y"]
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int32_t[:] months = fields["M"]
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int32_t[:] days = fields["D"]
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for y, m, d, wd in zip(years, months, days, weekdays):
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if calendar_start:
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calendar_start &= d == 1
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if business_start:
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business_start &= d == 1 or (d <= 3 and wd == 0)
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if calendar_end or business_end:
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daysinmonth = get_days_in_month(y, m)
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cal = d == daysinmonth
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if calendar_end:
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calendar_end &= cal
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if business_end:
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business_end &= cal or (daysinmonth - d < 3 and wd == 4)
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elif not calendar_start and not business_start:
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break
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if calendar_end:
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return "ce"
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elif business_end:
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return "be"
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elif calendar_start:
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return "cs"
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elif business_start:
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return "bs"
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else:
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return None
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def get_date_name_field(
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const int64_t[:] dtindex,
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str field,
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object locale=None,
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NPY_DATETIMEUNIT reso=NPY_FR_ns,
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):
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"""
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Given a int64-based datetime index, return array of strings of date
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name based on requested field (e.g. day_name)
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"""
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cdef:
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Py_ssize_t i
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cnp.npy_intp count = dtindex.shape[0]
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ndarray[object] out, names
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npy_datetimestruct dts
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int dow
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out = cnp.PyArray_EMPTY(1, &count, cnp.NPY_OBJECT, 0)
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if field == "day_name":
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if locale is None:
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names = np.array(DAYS_FULL, dtype=np.object_)
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else:
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names = np.array(_get_locale_names("f_weekday", locale),
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dtype=np.object_)
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = np.nan
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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dow = dayofweek(dts.year, dts.month, dts.day)
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out[i] = names[dow].capitalize()
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elif field == "month_name":
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if locale is None:
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names = np.array(MONTHS_FULL, dtype=np.object_)
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else:
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names = np.array(_get_locale_names("f_month", locale),
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dtype=np.object_)
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = np.nan
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = names[dts.month].capitalize()
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else:
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raise ValueError(f"Field {field} not supported")
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return out
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cdef bint _is_on_month(int month, int compare_month, int modby) noexcept nogil:
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"""
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Analogous to DateOffset.is_on_offset checking for the month part of a date.
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"""
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if modby == 1:
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return True
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elif modby == 3:
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return (month - compare_month) % 3 == 0
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else:
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return month == compare_month
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def get_start_end_field(
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const int64_t[:] dtindex,
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str field,
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str freqstr=None,
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int month_kw=12,
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NPY_DATETIMEUNIT reso=NPY_FR_ns,
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):
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"""
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Given an int64-based datetime index return array of indicators
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of whether timestamps are at the start/end of the month/quarter/year
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(defined by frequency).
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Parameters
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----------
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dtindex : ndarray[int64]
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field : str
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frestr : str or None, default None
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month_kw : int, default 12
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reso : NPY_DATETIMEUNIT, default NPY_FR_ns
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Returns
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-------
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ndarray[bool]
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"""
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cdef:
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Py_ssize_t i
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int count = dtindex.shape[0]
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bint is_business = 0
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int end_month = 12
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int start_month = 1
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ndarray[int8_t] out
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npy_datetimestruct dts
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int compare_month, modby
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out = np.zeros(count, dtype="int8")
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if freqstr:
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if freqstr == "C":
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raise ValueError(f"Custom business days is not supported by {field}")
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is_business = freqstr[0] == "B"
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# YearBegin(), BYearBegin() use month = starting month of year.
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# QuarterBegin(), BQuarterBegin() use startingMonth = starting
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# month of year. Other offsets use month, startingMonth as ending
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# month of year.
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if (freqstr[0:2] in ["MS", "QS", "AS"]) or (
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freqstr[1:3] in ["MS", "QS", "AS"]):
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end_month = 12 if month_kw == 1 else month_kw - 1
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start_month = month_kw
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else:
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end_month = month_kw
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start_month = (end_month % 12) + 1
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else:
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end_month = 12
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start_month = 1
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compare_month = start_month if "start" in field else end_month
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if "month" in field:
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modby = 1
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elif "quarter" in field:
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modby = 3
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else:
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modby = 12
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if field in ["is_month_start", "is_quarter_start", "is_year_start"]:
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if is_business:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = 0
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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if _is_on_month(dts.month, compare_month, modby) and (
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dts.day == get_firstbday(dts.year, dts.month)):
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out[i] = 1
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else:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = 0
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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if _is_on_month(dts.month, compare_month, modby) and dts.day == 1:
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out[i] = 1
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elif field in ["is_month_end", "is_quarter_end", "is_year_end"]:
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if is_business:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = 0
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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if _is_on_month(dts.month, compare_month, modby) and (
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dts.day == get_lastbday(dts.year, dts.month)):
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out[i] = 1
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else:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = 0
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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if _is_on_month(dts.month, compare_month, modby) and (
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dts.day == get_days_in_month(dts.year, dts.month)):
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out[i] = 1
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else:
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raise ValueError(f"Field {field} not supported")
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return out.view(bool)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def get_date_field(
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const int64_t[:] dtindex,
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str field,
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NPY_DATETIMEUNIT reso=NPY_FR_ns,
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):
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"""
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Given a int64-based datetime index, extract the year, month, etc.,
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field and return an array of these values.
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"""
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cdef:
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Py_ssize_t i, count = len(dtindex)
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ndarray[int32_t] out
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npy_datetimestruct dts
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out = np.empty(count, dtype="i4")
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if field == "Y":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.year
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return out
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elif field == "M":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.month
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return out
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elif field == "D":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.day
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return out
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elif field == "h":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.hour
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# TODO: can we de-dup with period.pyx <accessor>s?
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return out
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elif field == "m":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.min
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return out
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elif field == "s":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.sec
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return out
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elif field == "us":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.us
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return out
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elif field == "ns":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.ps // 1000
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return out
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elif field == "doy":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = get_day_of_year(dts.year, dts.month, dts.day)
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return out
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elif field == "dow":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dayofweek(dts.year, dts.month, dts.day)
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return out
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elif field == "woy":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = get_week_of_year(dts.year, dts.month, dts.day)
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return out
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elif field == "q":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = dts.month
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out[i] = ((out[i] - 1) // 3) + 1
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return out
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elif field == "dim":
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with nogil:
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for i in range(count):
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if dtindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
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out[i] = get_days_in_month(dts.year, dts.month)
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return out
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elif field == "is_leap_year":
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return isleapyear_arr(get_date_field(dtindex, "Y", reso=reso))
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raise ValueError(f"Field {field} not supported")
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def get_timedelta_field(
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const int64_t[:] tdindex,
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str field,
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NPY_DATETIMEUNIT reso=NPY_FR_ns,
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):
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"""
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|
Given a int64-based timedelta index, extract the days, hrs, sec.,
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field and return an array of these values.
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"""
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cdef:
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Py_ssize_t i, count = len(tdindex)
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ndarray[int32_t] out
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pandas_timedeltastruct tds
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out = np.empty(count, dtype="i4")
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if field == "seconds":
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with nogil:
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for i in range(count):
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if tdindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_timedelta_to_timedeltastruct(tdindex[i], reso, &tds)
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out[i] = tds.seconds
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return out
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elif field == "microseconds":
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with nogil:
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for i in range(count):
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if tdindex[i] == NPY_NAT:
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out[i] = -1
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continue
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pandas_timedelta_to_timedeltastruct(tdindex[i], reso, &tds)
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out[i] = tds.microseconds
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return out
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elif field == "nanoseconds":
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with nogil:
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for i in range(count):
|
|
if tdindex[i] == NPY_NAT:
|
|
out[i] = -1
|
|
continue
|
|
|
|
pandas_timedelta_to_timedeltastruct(tdindex[i], reso, &tds)
|
|
out[i] = tds.nanoseconds
|
|
return out
|
|
|
|
raise ValueError(f"Field {field} not supported")
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def get_timedelta_days(
|
|
const int64_t[:] tdindex,
|
|
NPY_DATETIMEUNIT reso=NPY_FR_ns,
|
|
):
|
|
"""
|
|
Given a int64-based timedelta index, extract the days,
|
|
field and return an array of these values.
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, count = len(tdindex)
|
|
ndarray[int64_t] out
|
|
pandas_timedeltastruct tds
|
|
|
|
out = np.empty(count, dtype="i8")
|
|
|
|
with nogil:
|
|
for i in range(count):
|
|
if tdindex[i] == NPY_NAT:
|
|
out[i] = -1
|
|
continue
|
|
|
|
pandas_timedelta_to_timedeltastruct(tdindex[i], reso, &tds)
|
|
out[i] = tds.days
|
|
return out
|
|
|
|
|
|
cpdef isleapyear_arr(ndarray years):
|
|
"""vectorized version of isleapyear; NaT evaluates as False"""
|
|
cdef:
|
|
ndarray[int8_t] out
|
|
|
|
out = np.zeros(len(years), dtype="int8")
|
|
out[np.logical_or(years % 400 == 0,
|
|
np.logical_and(years % 4 == 0,
|
|
years % 100 > 0))] = 1
|
|
return out.view(bool)
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def build_isocalendar_sarray(const int64_t[:] dtindex, NPY_DATETIMEUNIT reso):
|
|
"""
|
|
Given a int64-based datetime array, return the ISO 8601 year, week, and day
|
|
as a structured array.
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, count = len(dtindex)
|
|
npy_datetimestruct dts
|
|
ndarray[uint32_t] iso_years, iso_weeks, days
|
|
iso_calendar_t ret_val
|
|
|
|
sa_dtype = [
|
|
("year", "u4"),
|
|
("week", "u4"),
|
|
("day", "u4"),
|
|
]
|
|
|
|
out = np.empty(count, dtype=sa_dtype)
|
|
|
|
iso_years = out["year"]
|
|
iso_weeks = out["week"]
|
|
days = out["day"]
|
|
|
|
with nogil:
|
|
for i in range(count):
|
|
if dtindex[i] == NPY_NAT:
|
|
ret_val = 0, 0, 0
|
|
else:
|
|
pandas_datetime_to_datetimestruct(dtindex[i], reso, &dts)
|
|
ret_val = get_iso_calendar(dts.year, dts.month, dts.day)
|
|
|
|
iso_years[i] = ret_val[0]
|
|
iso_weeks[i] = ret_val[1]
|
|
days[i] = ret_val[2]
|
|
return out
|
|
|
|
|
|
def _get_locale_names(name_type: str, locale: object = None):
|
|
"""
|
|
Returns an array of localized day or month names.
|
|
|
|
Parameters
|
|
----------
|
|
name_type : str
|
|
Attribute of LocaleTime() in which to return localized names.
|
|
locale : str
|
|
|
|
Returns
|
|
-------
|
|
list of locale names
|
|
"""
|
|
with set_locale(locale, LC_TIME):
|
|
return getattr(LocaleTime(), name_type)
|
|
|
|
|
|
# ---------------------------------------------------------------------
|
|
# Rounding
|
|
|
|
|
|
class RoundTo:
|
|
"""
|
|
enumeration defining the available rounding modes
|
|
|
|
Attributes
|
|
----------
|
|
MINUS_INFTY
|
|
round towards -∞, or floor [2]_
|
|
PLUS_INFTY
|
|
round towards +∞, or ceil [3]_
|
|
NEAREST_HALF_EVEN
|
|
round to nearest, tie-break half to even [6]_
|
|
NEAREST_HALF_MINUS_INFTY
|
|
round to nearest, tie-break half to -∞ [5]_
|
|
NEAREST_HALF_PLUS_INFTY
|
|
round to nearest, tie-break half to +∞ [4]_
|
|
|
|
|
|
References
|
|
----------
|
|
.. [1] "Rounding - Wikipedia"
|
|
https://en.wikipedia.org/wiki/Rounding
|
|
.. [2] "Rounding down"
|
|
https://en.wikipedia.org/wiki/Rounding#Rounding_down
|
|
.. [3] "Rounding up"
|
|
https://en.wikipedia.org/wiki/Rounding#Rounding_up
|
|
.. [4] "Round half up"
|
|
https://en.wikipedia.org/wiki/Rounding#Round_half_up
|
|
.. [5] "Round half down"
|
|
https://en.wikipedia.org/wiki/Rounding#Round_half_down
|
|
.. [6] "Round half to even"
|
|
https://en.wikipedia.org/wiki/Rounding#Round_half_to_even
|
|
"""
|
|
@property
|
|
def MINUS_INFTY(self) -> int:
|
|
return 0
|
|
|
|
@property
|
|
def PLUS_INFTY(self) -> int:
|
|
return 1
|
|
|
|
@property
|
|
def NEAREST_HALF_EVEN(self) -> int:
|
|
return 2
|
|
|
|
@property
|
|
def NEAREST_HALF_PLUS_INFTY(self) -> int:
|
|
return 3
|
|
|
|
@property
|
|
def NEAREST_HALF_MINUS_INFTY(self) -> int:
|
|
return 4
|
|
|
|
|
|
cdef ndarray[int64_t] _floor_int64(const int64_t[:] values, int64_t unit):
|
|
cdef:
|
|
Py_ssize_t i, n = len(values)
|
|
ndarray[int64_t] result = np.empty(n, dtype="i8")
|
|
int64_t res, value
|
|
|
|
with cython.overflowcheck(True):
|
|
for i in range(n):
|
|
value = values[i]
|
|
if value == NPY_NAT:
|
|
res = NPY_NAT
|
|
else:
|
|
res = value - value % unit
|
|
result[i] = res
|
|
|
|
return result
|
|
|
|
|
|
cdef ndarray[int64_t] _ceil_int64(const int64_t[:] values, int64_t unit):
|
|
cdef:
|
|
Py_ssize_t i, n = len(values)
|
|
ndarray[int64_t] result = np.empty(n, dtype="i8")
|
|
int64_t res, value, remainder
|
|
|
|
with cython.overflowcheck(True):
|
|
for i in range(n):
|
|
value = values[i]
|
|
|
|
if value == NPY_NAT:
|
|
res = NPY_NAT
|
|
else:
|
|
remainder = value % unit
|
|
if remainder == 0:
|
|
res = value
|
|
else:
|
|
res = value + (unit - remainder)
|
|
|
|
result[i] = res
|
|
|
|
return result
|
|
|
|
|
|
cdef ndarray[int64_t] _rounddown_int64(values, int64_t unit):
|
|
return _ceil_int64(values - unit // 2, unit)
|
|
|
|
|
|
cdef ndarray[int64_t] _roundup_int64(values, int64_t unit):
|
|
return _floor_int64(values + unit // 2, unit)
|
|
|
|
|
|
cdef ndarray[int64_t] _round_nearest_int64(const int64_t[:] values, int64_t unit):
|
|
cdef:
|
|
Py_ssize_t i, n = len(values)
|
|
ndarray[int64_t] result = np.empty(n, dtype="i8")
|
|
int64_t res, value, half, remainder, quotient
|
|
|
|
half = unit // 2
|
|
|
|
with cython.overflowcheck(True):
|
|
for i in range(n):
|
|
value = values[i]
|
|
|
|
if value == NPY_NAT:
|
|
res = NPY_NAT
|
|
else:
|
|
quotient, remainder = divmod(value, unit)
|
|
if remainder > half:
|
|
res = value + (unit - remainder)
|
|
elif remainder == half and quotient % 2:
|
|
res = value + (unit - remainder)
|
|
else:
|
|
res = value - remainder
|
|
|
|
result[i] = res
|
|
|
|
return result
|
|
|
|
|
|
def round_nsint64(values: np.ndarray, mode: RoundTo, nanos: int) -> np.ndarray:
|
|
"""
|
|
Applies rounding mode at given frequency
|
|
|
|
Parameters
|
|
----------
|
|
values : np.ndarray[int64_t]`
|
|
mode : instance of `RoundTo` enumeration
|
|
nanos : np.int64
|
|
Freq to round to, expressed in nanoseconds
|
|
|
|
Returns
|
|
-------
|
|
np.ndarray[int64_t]
|
|
"""
|
|
cdef:
|
|
int64_t unit = nanos
|
|
|
|
if mode == RoundTo.MINUS_INFTY:
|
|
return _floor_int64(values, unit)
|
|
elif mode == RoundTo.PLUS_INFTY:
|
|
return _ceil_int64(values, unit)
|
|
elif mode == RoundTo.NEAREST_HALF_MINUS_INFTY:
|
|
return _rounddown_int64(values, unit)
|
|
elif mode == RoundTo.NEAREST_HALF_PLUS_INFTY:
|
|
return _roundup_int64(values, unit)
|
|
elif mode == RoundTo.NEAREST_HALF_EVEN:
|
|
# for odd unit there is no need of a tie break
|
|
if unit % 2:
|
|
return _rounddown_int64(values, unit)
|
|
return _round_nearest_int64(values, unit)
|
|
|
|
# if/elif above should catch all rounding modes defined in enum 'RoundTo':
|
|
# if flow of control arrives here, it is a bug
|
|
raise ValueError("round_nsint64 called with an unrecognized rounding mode")
|
|
|