aoc-2022/venv/Lib/site-packages/pandas/_libs/tslibs/np_datetime.pyx

607 lines
19 KiB
Cython

cimport cython
from cpython.datetime cimport (
PyDateTime_CheckExact,
PyDateTime_DATE_GET_HOUR,
PyDateTime_DATE_GET_MICROSECOND,
PyDateTime_DATE_GET_MINUTE,
PyDateTime_DATE_GET_SECOND,
PyDateTime_GET_DAY,
PyDateTime_GET_MONTH,
PyDateTime_GET_YEAR,
import_datetime,
)
from cpython.object cimport (
Py_EQ,
Py_GE,
Py_GT,
Py_LE,
Py_LT,
Py_NE,
)
import_datetime()
import numpy as np
cimport numpy as cnp
cnp.import_array()
from numpy cimport (
int64_t,
ndarray,
uint8_t,
)
from pandas._libs.tslibs.util cimport get_c_string_buf_and_size
cdef extern from "src/datetime/np_datetime.h":
int cmp_npy_datetimestruct(npy_datetimestruct *a,
npy_datetimestruct *b)
# AS, FS, PS versions exist but are not imported because they are not used.
npy_datetimestruct _NS_MIN_DTS, _NS_MAX_DTS
npy_datetimestruct _US_MIN_DTS, _US_MAX_DTS
npy_datetimestruct _MS_MIN_DTS, _MS_MAX_DTS
npy_datetimestruct _S_MIN_DTS, _S_MAX_DTS
npy_datetimestruct _M_MIN_DTS, _M_MAX_DTS
PyArray_DatetimeMetaData get_datetime_metadata_from_dtype(cnp.PyArray_Descr *dtype);
cdef extern from "src/datetime/np_datetime_strings.h":
int parse_iso_8601_datetime(const char *str, int len, int want_exc,
npy_datetimestruct *out,
NPY_DATETIMEUNIT *out_bestunit,
int *out_local, int *out_tzoffset)
# ----------------------------------------------------------------------
# numpy object inspection
cdef inline npy_datetime get_datetime64_value(object obj) nogil:
"""
returns the int64 value underlying scalar numpy datetime64 object
Note that to interpret this as a datetime, the corresponding unit is
also needed. That can be found using `get_datetime64_unit`.
"""
return (<PyDatetimeScalarObject*>obj).obval
cdef inline npy_timedelta get_timedelta64_value(object obj) nogil:
"""
returns the int64 value underlying scalar numpy timedelta64 object
"""
return (<PyTimedeltaScalarObject*>obj).obval
cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) nogil:
"""
returns the unit part of the dtype for a numpy datetime64 object.
"""
return <NPY_DATETIMEUNIT>(<PyDatetimeScalarObject*>obj).obmeta.base
cdef NPY_DATETIMEUNIT get_unit_from_dtype(cnp.dtype dtype):
# NB: caller is responsible for ensuring this is *some* datetime64 or
# timedelta64 dtype, otherwise we can segfault
cdef:
cnp.PyArray_Descr* descr = <cnp.PyArray_Descr*>dtype
PyArray_DatetimeMetaData meta
meta = get_datetime_metadata_from_dtype(descr)
return meta.base
def py_get_unit_from_dtype(dtype):
# for testing get_unit_from_dtype; adds 896 bytes to the .so file.
return get_unit_from_dtype(dtype)
def is_unitless(dtype: cnp.dtype) -> bool:
"""
Check if a datetime64 or timedelta64 dtype has no attached unit.
"""
if dtype.type_num not in [cnp.NPY_DATETIME, cnp.NPY_TIMEDELTA]:
raise ValueError("is_unitless dtype must be datetime64 or timedelta64")
cdef:
NPY_DATETIMEUNIT unit = get_unit_from_dtype(dtype)
return unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
# ----------------------------------------------------------------------
# Comparison
cdef bint cmp_dtstructs(
npy_datetimestruct* left, npy_datetimestruct* right, int op
):
cdef:
int cmp_res
cmp_res = cmp_npy_datetimestruct(left, right)
if op == Py_EQ:
return cmp_res == 0
if op == Py_NE:
return cmp_res != 0
if op == Py_GT:
return cmp_res == 1
if op == Py_LT:
return cmp_res == -1
if op == Py_GE:
return cmp_res == 1 or cmp_res == 0
else:
# i.e. op == Py_LE
return cmp_res == -1 or cmp_res == 0
cdef inline bint cmp_scalar(int64_t lhs, int64_t rhs, int op) except -1:
"""
cmp_scalar is a more performant version of PyObject_RichCompare
typed for int64_t arguments.
"""
if op == Py_EQ:
return lhs == rhs
elif op == Py_NE:
return lhs != rhs
elif op == Py_LT:
return lhs < rhs
elif op == Py_LE:
return lhs <= rhs
elif op == Py_GT:
return lhs > rhs
elif op == Py_GE:
return lhs >= rhs
class OutOfBoundsDatetime(ValueError):
"""
Raised when the datetime is outside the range that can be represented.
"""
pass
class OutOfBoundsTimedelta(ValueError):
"""
Raised when encountering a timedelta value that cannot be represented.
Representation should be within a timedelta64[ns].
"""
# Timedelta analogue to OutOfBoundsDatetime
pass
cdef get_implementation_bounds(NPY_DATETIMEUNIT reso, npy_datetimestruct *lower, npy_datetimestruct *upper):
if reso == NPY_FR_ns:
upper[0] = _NS_MAX_DTS
lower[0] = _NS_MIN_DTS
elif reso == NPY_FR_us:
upper[0] = _US_MAX_DTS
lower[0] = _US_MIN_DTS
elif reso == NPY_FR_ms:
upper[0] = _MS_MAX_DTS
lower[0] = _MS_MIN_DTS
elif reso == NPY_FR_s:
upper[0] = _S_MAX_DTS
lower[0] = _S_MIN_DTS
elif reso == NPY_FR_m:
upper[0] = _M_MAX_DTS
lower[0] = _M_MIN_DTS
else:
raise NotImplementedError(reso)
cdef check_dts_bounds(npy_datetimestruct *dts, NPY_DATETIMEUNIT unit=NPY_FR_ns):
"""Raises OutOfBoundsDatetime if the given date is outside the range that
can be represented by nanosecond-resolution 64-bit integers."""
cdef:
bint error = False
npy_datetimestruct cmp_upper, cmp_lower
get_implementation_bounds(unit, &cmp_lower, &cmp_upper)
if cmp_npy_datetimestruct(dts, &cmp_lower) == -1:
error = True
elif cmp_npy_datetimestruct(dts, &cmp_upper) == 1:
error = True
if error:
fmt = (f'{dts.year}-{dts.month:02d}-{dts.day:02d} '
f'{dts.hour:02d}:{dts.min:02d}:{dts.sec:02d}')
# TODO: "nanosecond" in the message assumes NPY_FR_ns
raise OutOfBoundsDatetime(f'Out of bounds nanosecond timestamp: {fmt}')
# ----------------------------------------------------------------------
# Conversion
cdef inline int64_t dtstruct_to_dt64(npy_datetimestruct* dts) nogil:
"""Convenience function to call npy_datetimestruct_to_datetime
with the by-far-most-common frequency NPY_FR_ns"""
return npy_datetimestruct_to_datetime(NPY_FR_ns, dts)
# just exposed for testing at the moment
def py_td64_to_tdstruct(int64_t td64, NPY_DATETIMEUNIT unit):
cdef:
pandas_timedeltastruct tds
pandas_timedelta_to_timedeltastruct(td64, unit, &tds)
return tds # <- returned as a dict to python
cdef inline void pydatetime_to_dtstruct(datetime dt, npy_datetimestruct *dts):
if PyDateTime_CheckExact(dt):
dts.year = PyDateTime_GET_YEAR(dt)
else:
# We use dt.year instead of PyDateTime_GET_YEAR because with Timestamp
# we override year such that PyDateTime_GET_YEAR is incorrect.
dts.year = dt.year
dts.month = PyDateTime_GET_MONTH(dt)
dts.day = PyDateTime_GET_DAY(dt)
dts.hour = PyDateTime_DATE_GET_HOUR(dt)
dts.min = PyDateTime_DATE_GET_MINUTE(dt)
dts.sec = PyDateTime_DATE_GET_SECOND(dt)
dts.us = PyDateTime_DATE_GET_MICROSECOND(dt)
dts.ps = dts.as = 0
cdef inline int64_t pydatetime_to_dt64(datetime val,
npy_datetimestruct *dts):
"""
Note we are assuming that the datetime object is timezone-naive.
"""
pydatetime_to_dtstruct(val, dts)
return dtstruct_to_dt64(dts)
cdef inline void pydate_to_dtstruct(date val, npy_datetimestruct *dts):
dts.year = PyDateTime_GET_YEAR(val)
dts.month = PyDateTime_GET_MONTH(val)
dts.day = PyDateTime_GET_DAY(val)
dts.hour = dts.min = dts.sec = dts.us = 0
dts.ps = dts.as = 0
return
cdef inline int64_t pydate_to_dt64(date val, npy_datetimestruct *dts):
pydate_to_dtstruct(val, dts)
return dtstruct_to_dt64(dts)
cdef inline int string_to_dts(
str val,
npy_datetimestruct* dts,
NPY_DATETIMEUNIT* out_bestunit,
int* out_local,
int* out_tzoffset,
bint want_exc,
) except? -1:
cdef:
Py_ssize_t length
const char* buf
buf = get_c_string_buf_and_size(val, &length)
return parse_iso_8601_datetime(buf, length, want_exc,
dts, out_bestunit, out_local, out_tzoffset)
cpdef ndarray astype_overflowsafe(
ndarray values,
cnp.dtype dtype,
bint copy=True,
bint round_ok=True,
):
"""
Convert an ndarray with datetime64[X] to datetime64[Y]
or timedelta64[X] to timedelta64[Y],
raising on overflow.
"""
if values.descr.type_num == dtype.type_num == cnp.NPY_DATETIME:
# i.e. dtype.kind == "M"
pass
elif values.descr.type_num == dtype.type_num == cnp.NPY_TIMEDELTA:
# i.e. dtype.kind == "m"
pass
else:
raise TypeError(
"astype_overflowsafe values.dtype and dtype must be either "
"both-datetime64 or both-timedelta64."
)
cdef:
NPY_DATETIMEUNIT from_unit = get_unit_from_dtype(values.dtype)
NPY_DATETIMEUNIT to_unit = get_unit_from_dtype(dtype)
if (
from_unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
or to_unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
):
# without raising explicitly here, we end up with a SystemError
# built-in function [...] returned a result with an error
raise ValueError(
"datetime64/timedelta64 values and dtype must have a unit specified"
)
if from_unit == to_unit:
# Check this before allocating result for perf, might save some memory
if copy:
return values.copy()
return values
elif from_unit > to_unit:
if round_ok:
# e.g. ns -> us, so there is no risk of overflow, so we can use
# numpy's astype safely. Note there _is_ risk of truncation.
return values.astype(dtype)
else:
iresult2 = astype_round_check(values.view("i8"), from_unit, to_unit)
return iresult2.view(dtype)
if (<object>values).dtype.byteorder == ">":
# GH#29684 we incorrectly get OutOfBoundsDatetime if we dont swap
values = values.astype(values.dtype.newbyteorder("<"))
cdef:
ndarray i8values = values.view("i8")
# equiv: result = np.empty((<object>values).shape, dtype="i8")
ndarray iresult = cnp.PyArray_EMPTY(
values.ndim, values.shape, cnp.NPY_INT64, 0
)
cnp.broadcast mi = cnp.PyArray_MultiIterNew2(iresult, i8values)
Py_ssize_t i, N = values.size
int64_t value, new_value
npy_datetimestruct dts
bint is_td = dtype.type_num == cnp.NPY_TIMEDELTA
for i in range(N):
# Analogous to: item = values[i]
value = (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
if value == NPY_DATETIME_NAT:
new_value = NPY_DATETIME_NAT
else:
pandas_datetime_to_datetimestruct(value, from_unit, &dts)
try:
check_dts_bounds(&dts, to_unit)
except OutOfBoundsDatetime as err:
if is_td:
from_abbrev = np.datetime_data(values.dtype)[0]
np_val = np.timedelta64(value, from_abbrev)
msg = (
"Cannot convert {np_val} to {dtype} without overflow"
.format(np_val=str(np_val), dtype=str(dtype))
)
raise OutOfBoundsTimedelta(msg) from err
else:
raise
new_value = npy_datetimestruct_to_datetime(to_unit, &dts)
# Analogous to: iresult[i] = new_value
(<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 0))[0] = new_value
cnp.PyArray_MultiIter_NEXT(mi)
return iresult.view(dtype)
# TODO: try to upstream this fix to numpy
def compare_mismatched_resolutions(ndarray left, ndarray right, op):
"""
Overflow-safe comparison of timedelta64/datetime64 with mismatched resolutions.
>>> left = np.array([500], dtype="M8[Y]")
>>> right = np.array([0], dtype="M8[ns]")
>>> left < right # <- wrong!
array([ True])
"""
if left.dtype.kind != right.dtype.kind or left.dtype.kind not in ["m", "M"]:
raise ValueError("left and right must both be timedelta64 or both datetime64")
cdef:
int op_code = op_to_op_code(op)
NPY_DATETIMEUNIT left_unit = get_unit_from_dtype(left.dtype)
NPY_DATETIMEUNIT right_unit = get_unit_from_dtype(right.dtype)
# equiv: result = np.empty((<object>left).shape, dtype="bool")
ndarray result = cnp.PyArray_EMPTY(
left.ndim, left.shape, cnp.NPY_BOOL, 0
)
ndarray lvalues = left.view("i8")
ndarray rvalues = right.view("i8")
cnp.broadcast mi = cnp.PyArray_MultiIterNew3(result, lvalues, rvalues)
int64_t lval, rval
bint res_value
Py_ssize_t i, N = left.size
npy_datetimestruct ldts, rdts
for i in range(N):
# Analogous to: lval = lvalues[i]
lval = (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
# Analogous to: rval = rvalues[i]
rval = (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 2))[0]
if lval == NPY_DATETIME_NAT or rval == NPY_DATETIME_NAT:
res_value = op_code == Py_NE
else:
pandas_datetime_to_datetimestruct(lval, left_unit, &ldts)
pandas_datetime_to_datetimestruct(rval, right_unit, &rdts)
res_value = cmp_dtstructs(&ldts, &rdts, op_code)
# Analogous to: result[i] = res_value
(<uint8_t*>cnp.PyArray_MultiIter_DATA(mi, 0))[0] = res_value
cnp.PyArray_MultiIter_NEXT(mi)
return result
import operator
cdef int op_to_op_code(op):
# TODO: should exist somewhere?
if op is operator.eq:
return Py_EQ
if op is operator.ne:
return Py_NE
if op is operator.le:
return Py_LE
if op is operator.lt:
return Py_LT
if op is operator.ge:
return Py_GE
if op is operator.gt:
return Py_GT
cdef ndarray astype_round_check(
ndarray i8values,
NPY_DATETIMEUNIT from_unit,
NPY_DATETIMEUNIT to_unit
):
# cases with from_unit > to_unit, e.g. ns->us, raise if the conversion
# involves truncation, e.g. 1500ns->1us
cdef:
Py_ssize_t i, N = i8values.size
# equiv: iresult = np.empty((<object>i8values).shape, dtype="i8")
ndarray iresult = cnp.PyArray_EMPTY(
i8values.ndim, i8values.shape, cnp.NPY_INT64, 0
)
cnp.broadcast mi = cnp.PyArray_MultiIterNew2(iresult, i8values)
# Note the arguments to_unit, from unit are swapped vs how they
# are passed when going to a higher-frequency reso.
int64_t mult = get_conversion_factor(to_unit, from_unit)
int64_t value, mod
for i in range(N):
# Analogous to: item = i8values[i]
value = (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
if value == NPY_DATETIME_NAT:
new_value = NPY_DATETIME_NAT
else:
new_value, mod = divmod(value, mult)
if mod != 0:
# TODO: avoid runtime import
from pandas._libs.tslibs.dtypes import npy_unit_to_abbrev
from_abbrev = npy_unit_to_abbrev(from_unit)
to_abbrev = npy_unit_to_abbrev(to_unit)
raise ValueError(
f"Cannot losslessly cast '{value} {from_abbrev}' to {to_abbrev}"
)
# Analogous to: iresult[i] = new_value
(<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 0))[0] = new_value
cnp.PyArray_MultiIter_NEXT(mi)
return iresult
@cython.overflowcheck(True)
cdef int64_t get_conversion_factor(NPY_DATETIMEUNIT from_unit, NPY_DATETIMEUNIT to_unit) except? -1:
"""
Find the factor by which we need to multiply to convert from from_unit to to_unit.
"""
if (
from_unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
or to_unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
):
raise ValueError("unit-less resolutions are not supported")
if from_unit > to_unit:
raise ValueError
if from_unit == to_unit:
return 1
if from_unit == NPY_DATETIMEUNIT.NPY_FR_W:
return 7 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_D, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_D:
return 24 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_h, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_h:
return 60 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_m, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_m:
return 60 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_s, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_s:
return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ms, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_ms:
return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_us, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_us:
return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ns, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_ns:
return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ps, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_ps:
return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_fs, to_unit)
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_fs:
return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_as, to_unit)
cdef int64_t convert_reso(
int64_t value,
NPY_DATETIMEUNIT from_reso,
NPY_DATETIMEUNIT to_reso,
bint round_ok,
) except? -1:
cdef:
int64_t res_value, mult, div, mod
if from_reso == to_reso:
return value
elif to_reso < from_reso:
# e.g. ns -> us, no risk of overflow, but can be lossy rounding
mult = get_conversion_factor(to_reso, from_reso)
div, mod = divmod(value, mult)
if mod > 0 and not round_ok:
raise ValueError("Cannot losslessly convert units")
# Note that when mod > 0, we follow np.timedelta64 in always
# rounding down.
res_value = div
elif (
from_reso == NPY_FR_Y
or from_reso == NPY_FR_M
or to_reso == NPY_FR_Y
or to_reso == NPY_FR_M
):
# Converting by multiplying isn't _quite_ right bc the number of
# seconds in a month/year isn't fixed.
res_value = _convert_reso_with_dtstruct(value, from_reso, to_reso)
else:
# e.g. ns -> us, risk of overflow, but no risk of lossy rounding
mult = get_conversion_factor(from_reso, to_reso)
with cython.overflowcheck(True):
# Note: caller is responsible for re-raising as OutOfBoundsTimedelta
res_value = value * mult
return res_value
cdef int64_t _convert_reso_with_dtstruct(
int64_t value,
NPY_DATETIMEUNIT from_unit,
NPY_DATETIMEUNIT to_unit,
) except? -1:
cdef:
npy_datetimestruct dts
pandas_datetime_to_datetimestruct(value, from_unit, &dts)
check_dts_bounds(&dts, to_unit)
return npy_datetimestruct_to_datetime(to_unit, &dts)