pruned venvs
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d3m1g0d
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# -*- coding: utf-8 -*-
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from datetime import timedelta
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from pandas.compat import zip
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from pandas import compat
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import re
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import numpy as np
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from pandas.core.dtypes.generic import ABCSeries
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from pandas.core.dtypes.common import (
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is_period_arraylike,
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is_timedelta64_dtype,
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is_datetime64_dtype)
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from pandas.tseries.offsets import DateOffset
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from pandas._libs.tslib import Timedelta
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import pandas._libs.tslibs.frequencies as libfreqs
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from pandas._libs.tslibs.frequencies import ( # noqa, semi-public API
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get_freq, get_base_alias, get_to_timestamp_base, get_freq_code,
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FreqGroup,
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is_subperiod, is_superperiod)
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from pandas._libs.tslibs.resolution import (Resolution,
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_FrequencyInferer,
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_TimedeltaFrequencyInferer)
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from pytz import AmbiguousTimeError
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RESO_NS = 0
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RESO_US = 1
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RESO_MS = 2
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RESO_SEC = 3
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RESO_MIN = 4
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RESO_HR = 5
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RESO_DAY = 6
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# ---------------------------------------------------------------------
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# Offset names ("time rules") and related functions
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from pandas._libs.tslibs.offsets import _offset_to_period_map # noqa:E402
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from pandas.tseries.offsets import (Nano, Micro, Milli, Second, # noqa
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Minute, Hour,
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Day, BDay, CDay, Week, MonthBegin,
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MonthEnd, BMonthBegin, BMonthEnd,
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QuarterBegin, QuarterEnd, BQuarterBegin,
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BQuarterEnd, YearBegin, YearEnd,
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BYearBegin, BYearEnd, prefix_mapping)
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try:
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cday = CDay()
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except NotImplementedError:
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cday = None
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#: cache of previously seen offsets
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_offset_map = {}
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def get_period_alias(offset_str):
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""" alias to closest period strings BQ->Q etc"""
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return _offset_to_period_map.get(offset_str, None)
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_name_to_offset_map = {'days': Day(1),
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'hours': Hour(1),
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'minutes': Minute(1),
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'seconds': Second(1),
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'milliseconds': Milli(1),
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'microseconds': Micro(1),
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'nanoseconds': Nano(1)}
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def to_offset(freq):
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"""
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Return DateOffset object from string or tuple representation
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or datetime.timedelta object
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Parameters
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----------
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freq : str, tuple, datetime.timedelta, DateOffset or None
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Returns
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-------
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delta : DateOffset
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None if freq is None
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Raises
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------
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ValueError
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If freq is an invalid frequency
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See Also
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--------
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pandas.DateOffset
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Examples
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--------
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>>> to_offset('5min')
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<5 * Minutes>
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>>> to_offset('1D1H')
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<25 * Hours>
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>>> to_offset(('W', 2))
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<2 * Weeks: weekday=6>
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>>> to_offset((2, 'B'))
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<2 * BusinessDays>
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>>> to_offset(datetime.timedelta(days=1))
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<Day>
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>>> to_offset(Hour())
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<Hour>
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"""
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if freq is None:
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return None
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if isinstance(freq, DateOffset):
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return freq
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if isinstance(freq, tuple):
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name = freq[0]
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stride = freq[1]
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if isinstance(stride, compat.string_types):
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name, stride = stride, name
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name, _ = libfreqs._base_and_stride(name)
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delta = get_offset(name) * stride
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elif isinstance(freq, timedelta):
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delta = None
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freq = Timedelta(freq)
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try:
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for name in freq.components._fields:
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offset = _name_to_offset_map[name]
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stride = getattr(freq.components, name)
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if stride != 0:
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offset = stride * offset
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if delta is None:
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delta = offset
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else:
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delta = delta + offset
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except Exception:
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raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(freq))
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else:
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delta = None
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stride_sign = None
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try:
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splitted = re.split(libfreqs.opattern, freq)
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if splitted[-1] != '' and not splitted[-1].isspace():
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# the last element must be blank
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raise ValueError('last element must be blank')
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for sep, stride, name in zip(splitted[0::4], splitted[1::4],
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splitted[2::4]):
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if sep != '' and not sep.isspace():
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raise ValueError('separator must be spaces')
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prefix = libfreqs._lite_rule_alias.get(name) or name
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if stride_sign is None:
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stride_sign = -1 if stride.startswith('-') else 1
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if not stride:
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stride = 1
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if prefix in Resolution._reso_str_bump_map.keys():
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stride, name = Resolution.get_stride_from_decimal(
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float(stride), prefix
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)
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stride = int(stride)
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offset = get_offset(name)
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offset = offset * int(np.fabs(stride) * stride_sign)
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if delta is None:
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delta = offset
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else:
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delta = delta + offset
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except Exception:
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raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(freq))
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if delta is None:
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raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(freq))
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return delta
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def get_offset(name):
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"""
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Return DateOffset object associated with rule name
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Examples
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--------
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get_offset('EOM') --> BMonthEnd(1)
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"""
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if name not in libfreqs._dont_uppercase:
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name = name.upper()
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name = libfreqs._lite_rule_alias.get(name, name)
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name = libfreqs._lite_rule_alias.get(name.lower(), name)
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else:
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name = libfreqs._lite_rule_alias.get(name, name)
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if name not in _offset_map:
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try:
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split = name.split('-')
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klass = prefix_mapping[split[0]]
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# handles case where there's no suffix (and will TypeError if too
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# many '-')
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offset = klass._from_name(*split[1:])
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except (ValueError, TypeError, KeyError):
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# bad prefix or suffix
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raise ValueError(libfreqs._INVALID_FREQ_ERROR.format(name))
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# cache
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_offset_map[name] = offset
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# do not return cache because it's mutable
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return _offset_map[name].copy()
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getOffset = get_offset
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# ---------------------------------------------------------------------
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# Period codes
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def infer_freq(index, warn=True):
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"""
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Infer the most likely frequency given the input index. If the frequency is
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uncertain, a warning will be printed.
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Parameters
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----------
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index : DatetimeIndex or TimedeltaIndex
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if passed a Series will use the values of the series (NOT THE INDEX)
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warn : boolean, default True
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Returns
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-------
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freq : string or None
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None if no discernible frequency
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TypeError if the index is not datetime-like
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ValueError if there are less than three values.
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"""
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import pandas as pd
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if isinstance(index, ABCSeries):
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values = index._values
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if not (is_datetime64_dtype(values) or
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is_timedelta64_dtype(values) or
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values.dtype == object):
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raise TypeError("cannot infer freq from a non-convertible dtype "
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"on a Series of {dtype}".format(dtype=index.dtype))
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index = values
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if is_period_arraylike(index):
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raise TypeError("PeriodIndex given. Check the `freq` attribute "
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"instead of using infer_freq.")
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elif isinstance(index, pd.TimedeltaIndex):
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inferer = _TimedeltaFrequencyInferer(index, warn=warn)
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return inferer.get_freq()
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if isinstance(index, pd.Index) and not isinstance(index, pd.DatetimeIndex):
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if isinstance(index, (pd.Int64Index, pd.Float64Index)):
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raise TypeError("cannot infer freq from a non-convertible index "
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"type {type}".format(type=type(index)))
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index = index.values
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if not isinstance(index, pd.DatetimeIndex):
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try:
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index = pd.DatetimeIndex(index)
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except AmbiguousTimeError:
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index = pd.DatetimeIndex(index.asi8)
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inferer = _FrequencyInferer(index, warn=warn)
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return inferer.get_freq()
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