laywerrobot/lib/python3.6/site-packages/pandas/tseries/frequencies.py
2020-08-27 21:55:39 +02:00

270 lines
8.2 KiB
Python

# -*- coding: utf-8 -*-
from datetime import timedelta
from pandas.compat import zip
from pandas import compat
import re
import numpy as np
from pandas.core.dtypes.generic import ABCSeries
from pandas.core.dtypes.common import (
is_period_arraylike,
is_timedelta64_dtype,
is_datetime64_dtype)
from pandas.tseries.offsets import DateOffset
from pandas._libs.tslib import Timedelta
import pandas._libs.tslibs.frequencies as libfreqs
from pandas._libs.tslibs.frequencies import ( # noqa, semi-public API
get_freq, get_base_alias, get_to_timestamp_base, get_freq_code,
FreqGroup,
is_subperiod, is_superperiod)
from pandas._libs.tslibs.resolution import (Resolution,
_FrequencyInferer,
_TimedeltaFrequencyInferer)
from pytz import AmbiguousTimeError
RESO_NS = 0
RESO_US = 1
RESO_MS = 2
RESO_SEC = 3
RESO_MIN = 4
RESO_HR = 5
RESO_DAY = 6
# ---------------------------------------------------------------------
# Offset names ("time rules") and related functions
from pandas._libs.tslibs.offsets import _offset_to_period_map # noqa:E402
from pandas.tseries.offsets import (Nano, Micro, Milli, Second, # noqa
Minute, Hour,
Day, BDay, CDay, Week, MonthBegin,
MonthEnd, BMonthBegin, BMonthEnd,
QuarterBegin, QuarterEnd, BQuarterBegin,
BQuarterEnd, YearBegin, YearEnd,
BYearBegin, BYearEnd, prefix_mapping)
try:
cday = CDay()
except NotImplementedError:
cday = None
#: cache of previously seen offsets
_offset_map = {}
def get_period_alias(offset_str):
""" alias to closest period strings BQ->Q etc"""
return _offset_to_period_map.get(offset_str, None)
_name_to_offset_map = {'days': Day(1),
'hours': Hour(1),
'minutes': Minute(1),
'seconds': Second(1),
'milliseconds': Milli(1),
'microseconds': Micro(1),
'nanoseconds': Nano(1)}
def to_offset(freq):
"""
Return DateOffset object from string or tuple representation
or datetime.timedelta object
Parameters
----------
freq : str, tuple, datetime.timedelta, DateOffset or None
Returns
-------
delta : DateOffset
None if freq is None
Raises
------
ValueError
If freq is an invalid frequency
See Also
--------
pandas.DateOffset
Examples
--------
>>> to_offset('5min')
<5 * Minutes>
>>> to_offset('1D1H')
<25 * Hours>
>>> to_offset(('W', 2))
<2 * Weeks: weekday=6>
>>> to_offset((2, 'B'))
<2 * BusinessDays>
>>> to_offset(datetime.timedelta(days=1))
<Day>
>>> to_offset(Hour())
<Hour>
"""
if freq is None:
return None
if isinstance(freq, DateOffset):
return freq
if isinstance(freq, tuple):
name = freq[0]
stride = freq[1]
if isinstance(stride, compat.string_types):
name, stride = stride, name
name, _ = libfreqs._base_and_stride(name)
delta = get_offset(name) * stride
elif isinstance(freq, timedelta):
delta = None
freq = Timedelta(freq)
try:
for name in freq.components._fields:
offset = _name_to_offset_map[name]
stride = getattr(freq.components, name)
if stride != 0:
offset = stride * offset
if delta is None:
delta = offset
else:
delta = delta + offset
except Exception:
raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(freq))
else:
delta = None
stride_sign = None
try:
splitted = re.split(libfreqs.opattern, freq)
if splitted[-1] != '' and not splitted[-1].isspace():
# the last element must be blank
raise ValueError('last element must be blank')
for sep, stride, name in zip(splitted[0::4], splitted[1::4],
splitted[2::4]):
if sep != '' and not sep.isspace():
raise ValueError('separator must be spaces')
prefix = libfreqs._lite_rule_alias.get(name) or name
if stride_sign is None:
stride_sign = -1 if stride.startswith('-') else 1
if not stride:
stride = 1
if prefix in Resolution._reso_str_bump_map.keys():
stride, name = Resolution.get_stride_from_decimal(
float(stride), prefix
)
stride = int(stride)
offset = get_offset(name)
offset = offset * int(np.fabs(stride) * stride_sign)
if delta is None:
delta = offset
else:
delta = delta + offset
except Exception:
raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(freq))
if delta is None:
raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(freq))
return delta
def get_offset(name):
"""
Return DateOffset object associated with rule name
Examples
--------
get_offset('EOM') --> BMonthEnd(1)
"""
if name not in libfreqs._dont_uppercase:
name = name.upper()
name = libfreqs._lite_rule_alias.get(name, name)
name = libfreqs._lite_rule_alias.get(name.lower(), name)
else:
name = libfreqs._lite_rule_alias.get(name, name)
if name not in _offset_map:
try:
split = name.split('-')
klass = prefix_mapping[split[0]]
# handles case where there's no suffix (and will TypeError if too
# many '-')
offset = klass._from_name(*split[1:])
except (ValueError, TypeError, KeyError):
# bad prefix or suffix
raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(name))
# cache
_offset_map[name] = offset
# do not return cache because it's mutable
return _offset_map[name].copy()
getOffset = get_offset
# ---------------------------------------------------------------------
# Period codes
def infer_freq(index, warn=True):
"""
Infer the most likely frequency given the input index. If the frequency is
uncertain, a warning will be printed.
Parameters
----------
index : DatetimeIndex or TimedeltaIndex
if passed a Series will use the values of the series (NOT THE INDEX)
warn : boolean, default True
Returns
-------
freq : string or None
None if no discernible frequency
TypeError if the index is not datetime-like
ValueError if there are less than three values.
"""
import pandas as pd
if isinstance(index, ABCSeries):
values = index._values
if not (is_datetime64_dtype(values) or
is_timedelta64_dtype(values) or
values.dtype == object):
raise TypeError("cannot infer freq from a non-convertible dtype "
"on a Series of {dtype}".format(dtype=index.dtype))
index = values
if is_period_arraylike(index):
raise TypeError("PeriodIndex given. Check the `freq` attribute "
"instead of using infer_freq.")
elif isinstance(index, pd.TimedeltaIndex):
inferer = _TimedeltaFrequencyInferer(index, warn=warn)
return inferer.get_freq()
if isinstance(index, pd.Index) and not isinstance(index, pd.DatetimeIndex):
if isinstance(index, (pd.Int64Index, pd.Float64Index)):
raise TypeError("cannot infer freq from a non-convertible index "
"type {type}".format(type=type(index)))
index = index.values
if not isinstance(index, pd.DatetimeIndex):
try:
index = pd.DatetimeIndex(index)
except AmbiguousTimeError:
index = pd.DatetimeIndex(index.asi8)
inferer = _FrequencyInferer(index, warn=warn)
return inferer.get_freq()