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Merged
merged 1 commit into from
Nov 16, 2017
Merged

import from np_datetime instead of src/datetime #18312

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14 changes: 6 additions & 8 deletions pandas/_libs/period.pyx
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Expand Up @@ -19,31 +19,29 @@ from pandas.compat import PY2
cimport cython

from tslibs.np_datetime cimport (pandas_datetimestruct,
dtstruct_to_dt64, dt64_to_dtstruct)
from datetime cimport is_leapyear
dtstruct_to_dt64, dt64_to_dtstruct,
is_leapyear)


cimport util
from util cimport is_period_object, is_string_object, INT32_MIN

from lib cimport is_null_datetimelike
from pandas._libs import tslib
from pandas._libs.tslib import Timestamp, iNaT
from pandas._libs.tslib import Timestamp
from tslibs.timezones cimport (
is_utc, is_tzlocal, get_utcoffset, get_dst_info, maybe_get_tz)
is_utc, is_tzlocal, get_utcoffset, get_dst_info)
from tslibs.timedeltas cimport delta_to_nanoseconds

from tslibs.parsing import (parse_time_string, NAT_SENTINEL,
_get_rule_month, _MONTH_NUMBERS)
from tslibs.frequencies cimport get_freq_code
from tslibs.resolution import resolution, Resolution
from tslibs.nattype import nat_strings, NaT
from tslibs.nattype cimport _nat_scalar_rules
from tslibs.nattype import nat_strings, NaT, iNaT
from tslibs.nattype cimport _nat_scalar_rules, NPY_NAT

from pandas.tseries import offsets
from pandas.tseries import frequencies

cdef int64_t NPY_NAT = util.get_nat()

cdef extern from "period_helper.h":
ctypedef struct date_info:
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21 changes: 7 additions & 14 deletions pandas/_libs/tslib.pyx
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Expand Up @@ -38,14 +38,7 @@ from cpython.datetime cimport (PyDelta_Check, PyTZInfo_Check,
# import datetime C API
PyDateTime_IMPORT
# this is our datetime.pxd
from datetime cimport (
pandas_datetime_to_datetimestruct,
days_per_month_table,
PANDAS_DATETIMEUNIT,
_string_to_dts,
is_leapyear,
dayofweek,
PANDAS_FR_ns)
from datetime cimport pandas_datetime_to_datetimestruct, _string_to_dts

# stdlib datetime imports
from datetime import time as datetime_time
Expand All @@ -55,11 +48,14 @@ from tslibs.np_datetime cimport (check_dts_bounds,
reverse_ops,
cmp_scalar,
pandas_datetimestruct,
PANDAS_DATETIMEUNIT, PANDAS_FR_ns,
dt64_to_dtstruct, dtstruct_to_dt64,
pydatetime_to_dt64, pydate_to_dt64,
npy_datetime,
get_datetime64_unit, get_datetime64_value,
get_timedelta64_value)
get_timedelta64_value,
days_per_month_table,
dayofweek, is_leapyear)
from tslibs.np_datetime import OutOfBoundsDatetime

from .tslibs.parsing import parse_datetime_string
Expand All @@ -75,9 +71,6 @@ UTC = pytz.utc
import_array()


cdef int64_t NPY_NAT = util.get_nat()
iNaT = NPY_NAT

from tslibs.timedeltas cimport cast_from_unit, delta_to_nanoseconds
from tslibs.timedeltas import Timedelta
from tslibs.timezones cimport (
Expand All @@ -95,8 +88,8 @@ from tslibs.conversion cimport (tz_convert_single, _TSObject,
from tslibs.conversion import (tz_localize_to_utc,
tz_convert_single, date_normalize)

from tslibs.nattype import NaT, nat_strings
from tslibs.nattype cimport _checknull_with_nat
from tslibs.nattype import NaT, nat_strings, iNaT
from tslibs.nattype cimport _checknull_with_nat, NPY_NAT


cdef inline object create_timestamp_from_ts(
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11 changes: 5 additions & 6 deletions pandas/_libs/tslibs/conversion.pyx
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Expand Up @@ -20,15 +20,14 @@ PyDateTime_IMPORT

from np_datetime cimport (check_dts_bounds,
pandas_datetimestruct,
PANDAS_DATETIMEUNIT, PANDAS_FR_ns,
npy_datetime,
dt64_to_dtstruct, dtstruct_to_dt64,
get_datetime64_unit, get_datetime64_value,
pydatetime_to_dt64)

from datetime cimport (pandas_datetime_to_datetimestruct,
PANDAS_DATETIMEUNIT, PANDAS_FR_ns, npy_datetime,
_string_to_dts,
get_datetime64_unit, get_datetime64_value)
from datetime cimport pandas_datetime_to_datetimestruct, _string_to_dts

cimport util
from util cimport (is_string_object,
is_datetime64_object,
is_integer_object, is_float_object)
Expand All @@ -41,10 +40,10 @@ from timezones cimport (
from parsing import parse_datetime_string

from nattype import nat_strings, NaT
from nattype cimport NPY_NAT

# ----------------------------------------------------------------------
# Constants
cdef int64_t NPY_NAT = util.get_nat()

cdef int64_t DAY_NS = 86400000000000LL

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13 changes: 3 additions & 10 deletions pandas/_libs/tslibs/fields.pyx
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Expand Up @@ -18,16 +18,9 @@ np.import_array()


from np_datetime cimport (pandas_datetimestruct, pandas_timedeltastruct,
dt64_to_dtstruct, td64_to_tdstruct)

from datetime cimport (
days_per_month_table,
is_leapyear,
dayofweek)

cimport util

cdef int64_t NPY_NAT = util.get_nat()
dt64_to_dtstruct, td64_to_tdstruct,
days_per_month_table, is_leapyear, dayofweek)
from nattype cimport NPY_NAT


def build_field_sarray(ndarray[int64_t] dtindex):
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3 changes: 3 additions & 0 deletions pandas/_libs/tslibs/nattype.pxd
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@@ -1,6 +1,9 @@
# -*- coding: utf-8 -*-
# cython: profile=False

from numpy cimport int64_t
cdef int64_t NPY_NAT

cdef bint _nat_scalar_rules[6]

cdef bint _checknull_with_nat(object val)
1 change: 1 addition & 0 deletions pandas/_libs/tslibs/nattype.pyx
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Expand Up @@ -26,6 +26,7 @@ from util cimport (get_nat,
nat_strings = set(['NaT', 'nat', 'NAT', 'nan', 'NaN', 'NAN'])

cdef int64_t NPY_NAT = get_nat()
iNaT = NPY_NAT # python-visible constant

cdef bint _nat_scalar_rules[6]
_nat_scalar_rules[Py_EQ] = False
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5 changes: 5 additions & 0 deletions pandas/_libs/tslibs/np_datetime.pxd
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Expand Up @@ -50,6 +50,11 @@ cdef extern from "../src/datetime/np_datetime.h":
PANDAS_FR_fs
PANDAS_FR_as

int days_per_month_table[2][12]
int dayofweek(int y, int m, int d) nogil
int is_leapyear(int64_t year) nogil
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There are a couple of modules that use one or more of these three items but dont need the rest of the relatively heavy-weight src/datetime/np_datetime dependency. Ideally I'd prefer for these to be implemented in a dependency-free cython module.

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sure these are like dateutils



cdef int reverse_ops[6]

cdef bint cmp_scalar(int64_t lhs, int64_t rhs, int op) except -1
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6 changes: 2 additions & 4 deletions pandas/_libs/tslibs/strptime.pyx
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Expand Up @@ -36,11 +36,9 @@ from cpython.datetime cimport datetime
from np_datetime cimport (check_dts_bounds,
dtstruct_to_dt64, pandas_datetimestruct)

from util cimport is_string_object, get_nat
from util cimport is_string_object

cdef int64_t NPY_NAT = get_nat()

from nattype cimport _checknull_with_nat
from nattype cimport _checknull_with_nat, NPY_NAT
from nattype import nat_strings


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8 changes: 3 additions & 5 deletions pandas/_libs/tslibs/timedeltas.pyx
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Expand Up @@ -30,13 +30,11 @@ from np_datetime cimport (cmp_scalar, reverse_ops, td64_to_tdstruct,
pandas_timedeltastruct)

from nattype import nat_strings, NaT
from nattype cimport _checknull_with_nat
from nattype cimport _checknull_with_nat, NPY_NAT

# ----------------------------------------------------------------------
# Constants

cdef int64_t NPY_NAT = util.get_nat()

cdef int64_t DAY_NS = 86400000000000LL

# components named tuple
Expand Down Expand Up @@ -872,7 +870,7 @@ class Timedelta(_Timedelta):

if isinstance(value, Timedelta):
value = value.value
elif util.is_string_object(value):
elif is_string_object(value):
value = np.timedelta64(parse_timedelta_string(value))
elif PyDelta_Check(value):
value = convert_to_timedelta64(value, 'ns')
Expand All @@ -882,7 +880,7 @@ class Timedelta(_Timedelta):
value = value.astype('timedelta64[ns]')
elif hasattr(value, 'delta'):
value = np.timedelta64(delta_to_nanoseconds(value.delta), 'ns')
elif is_integer_object(value) or util.is_float_object(value):
elif is_integer_object(value) or is_float_object(value):
# unit=None is de-facto 'ns'
value = convert_to_timedelta64(value, unit)
elif _checknull_with_nat(value):
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3 changes: 2 additions & 1 deletion setup.py
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Expand Up @@ -553,13 +553,14 @@ def pxd(name):
'_libs.tslibs.conversion': {
'pyxfile': '_libs/tslibs/conversion',
'pxdfiles': ['_libs/src/util',
'_libs/tslibs/nattype',
'_libs/tslibs/timezones',
'_libs/tslibs/timedeltas'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.fields': {
'pyxfile': '_libs/tslibs/fields',
'pxdfiles': ['_libs/src/util'],
'pxdfiles': ['_libs/tslibs/nattype'],
'depends': tseries_depends,
'sources': np_datetime_sources},
'_libs.tslibs.frequencies': {
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