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日期和时间模块-fleming时区模块 ​

介绍 ​

This package contains Fleming, which contains a set of routines for doing datetime manipulation. Named after Sandford Fleming, the father of worldwide standard timezones, this package is meant to aid datetime manipulations with regards to timezones.

Fleming addresses some of the common difficulties with timezones and datetime objects, such as performing arithmetic and datetime truncation across a Daylight Savings Time border. It also provides utilities for generating date ranges and getting unix times with respect to timezones.

即：该软件包包含Fleming，其中包含一组用于执行日期时间操作的例程。 该软件包以全球标准时区之父桑福德·弗莱明（Sandford Fleming）的名字命名，旨在帮助对时区进行日期时间操纵。

弗莱明（Fleming）解决了时区和日期时间对象的一些常见困难，例如跨夏令时边界执行算术和日期时间截断。 它还提供了用于生成日期范围和获取相对于时区的Unix时间的实用程序。

Fleming接受pytz时区对象作为参数，并且假定用户对pytz有基本的了解。 如果你不了解pytz模块，可以参考模块-日期和时间模块之pytz时区模块了解有关pytz的更多信息。

安装 ​

sh
$ pip install fleming
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
Collecting fleming
  Downloading http://mirrors.aliyun.com/pypi/packages/c0/8c/42972e31f78e54dcd7fe74677439e03037b2ba11af994d65324791a27fe4/fleming-0.5.0-py2.py3-none-any.whl (9.7 kB)
Requirement already satisfied: python-dateutil>=2.2 in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from fleming) (2.8.1)
Requirement already satisfied: pytz>=2013.9 in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from fleming) (2020.1)
Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from python-dateutil>=2.2->fleming) (1.14.0)
Installing collected packages: fleming
Successfully installed fleming-0.5.0

查看fleming模块有哪些函数或方法：

py
$ python
Python 3.6.8 (v3.6.8:3c6b436a57, Dec 24 2018, 02:10:22)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import fleming
>>> fleming.
fleming.add_timedelta( fleming.convert_to_tz( fleming.floor(         fleming.unix_time(
fleming.ceil(          fleming.fleming        fleming.intervals(
>>> exit()

功能概述 ​

下面是此软件包中每个功能的简要说明。 之后，将对功能进行更详细的说明和高级用法。 单击功能名称以转到其详细说明。

  • convert_to_tz：将日期时间对象转换为指定时区的时间对象。
  • add_timedelta：将一个timedelta添加到datetime对象。
  • floor：将日期时间对象向下舍入到上一个时间间隔。
  • ceil：将日期时间对象四舍五入到下一个时间间隔。
  • interval：以给定的时间间隔获取时间范围。
  • unix_time：返回日期时间对象的unix时间戳。

convert_to_tz将日期时间对象转换指定时区的时间对象 ​

fleming.convert_to_tz(dt, tz, return_naive=False) 将一个datetime对象转换成另一个时区的datetime对象。

参数说明：

  • dt，datetime对象，如果datetime对象没有时区设置，则默认以UTC作为其时区。
  • tz，pytz模块的timezone对象，即需要转换到哪个时区去。
  • return_naive=False是否转换成naive datetime object即是否转换成无时区的datetime对象，默认是不转换，即保留时区信息。
python
$ ipython
Python 3.6.8 (v3.6.8:3c6b436a57, Dec 24 2018, 02:10:22)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.13.0 -- An enhanced Interactive Python. Type '?' for help.

# 导入模块
>>> from datetime import datetime

>>> from fleming import convert_to_tz

>>> from pytz import UTC, timezone

>>> datetime?
Init signature: datetime(self, /, *args, **kwargs)
Docstring:
datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]])

The year, month and day arguments are required. tzinfo may be None, or an
instance of a tzinfo subclass. The remaining arguments may be ints.
File:           /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/datetime.py
Type:           type
Subclasses:

# 构建标准UTC标准时间datetime对象
>>> dt_utc =  datetime(2020,7,15,12,45,20, tzinfo=UTC)

>>> dt_utc
datetime.datetime(2020, 7, 15, 12, 45, 20, tzinfo=<UTC>)

>>> print(dt_utc)
2020-07-15 12:45:20+00:00
      
# 创建中国时区对象
>>> china_timezone = timezone('Asia/Shanghai')

>>> china_timezone
<DstTzInfo 'Asia/Shanghai' LMT+8:06:00 STD>

# 将标准时间datetime对象转换到亚洲/上海时区来，得到中国的时间
>>> dt_local = convert_to_tz(dt_utc, china_timezone)

# 可以看到，正常转换成中国的时间，中国的时间比标准时间早8个小时
>>> dt_local
datetime.datetime(2020, 7, 15, 20, 45, 20, tzinfo=<DstTzInfo 'Asia/Shanghai' CST+8:00:00 STD>)

# 打印时间，最后带时区信息
>>> print(dt_local)
2020-07-15 20:45:20+08:00
      
# 注意，转换时对原来的datetime对象不会产生影响，只会产生一个新的对象
>>> dt_utc
datetime.datetime(2020, 7, 15, 12, 45, 20, tzinfo=<UTC>)

我们再定义一个时区，将UTC标准时间对象转换成不带时区对象的datetime对象：

python
# 创建美国东部时区
>>> eastern = timezone('US/Eastern')

>>> eastern
<DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>

# 将UTC标准时间转换成美国东部时区时间对象，并且不带时区信息
>>> dt_eastern = convert_to_tz(dt_utc, eastern, return_naive=True)

# 可以看到，正常转换成美国东部的时间，美国东部时间比标准时间晚4个小时，此时datetime对象中已经没有时区信息
>>> dt_eastern
datetime.datetime(2020, 7, 15, 8, 45, 20)

# 打印时间，最后没有时区信息
>>> print(dt_eastern)
2020-07-15 08:45:20

# 注意，转换时对原来的datetime对象不会产生影响，只会产生一个新的对象
>>> dt_utc
datetime.datetime(2020, 7, 15, 12, 45, 20, tzinfo=<UTC>)

这个时间，再不应该对转换后的时间再进行转换，转换过程中有可能得到的结果不是你想要的！

我们尝试再进一步转换：

python
# 将中国的本地时间转换成美国东部时间
>>> local_2_eastern = convert_to_tz(dt_local, eastern)

# 因为dt_local中带有时区信息，所以此时转换到美国东部时间时，转换结果正常，得到早上8:45的时间，与上面的dt_eastern时间是一样的
>>> local_2_eastern
datetime.datetime(2020, 7, 15, 8, 45, 20, tzinfo=<DstTzInfo 'US/Eastern' EDT-1 day, 20:00:00 DST>)

# 打印美国东部时间，此时结果是正确的！
>>> print(local_2_eastern)
2020-07-15 08:45:20-04:00

# 将美国东部时间转换成中国时间，此时因为dt_eastern并没有时区信息，得到的结果是16:45，而不是20:45，可以发现此处错误了！
# 原因：因为dt_eastern并没有带时区信息，所以conver_to_tz函数将dt_eastern当做了UTC标准时间，并没有把它当前美国东部(-04:00)时区时间，dt_eastern时间是早上8:45，按照中国时间比UTC标准时间早8小时计算，转换后中国时间就是16:45，也就是下面的结果，而实际上现在中国是20:45，也就是中间相差了4小时呢！！！
>>> eastern_2_local = convert_to_tz(dt_eastern, china_timezone)

# 这是一个错误的结果！
>>> eastern_2_local
datetime.datetime(2020, 7, 15, 16, 45, 20, tzinfo=<DstTzInfo 'Asia/Shanghai' CST+8:00:00 STD>)

>>>

总结，无论什么时间，只要涉及到时间对象的转换，一定通过UTC标准时间进行计算！！对UTC标准时间进行时区转换再得到最好要的结果！

add_timedelta给datetime对象增加时间增量 ​

fleming.add_timedelta(dt, td, within_tz=None)给datetime对象增加时间增量。

参数说明：

  • dt，datetime对象，如果datetime对象没有时区设置，则默认以UTC作为其时区。
  • td，timedelta对象，需要添加的时间增量。
  • within_tz，pytz模块的timezone对象，如果指定该参数，则dt将在日期时间算术之前转换为该时区，然后再转换回其原始时区。
python
>>> from datetime import timedelta

>>> from fleming import add_timedelta

# UTC标准时间,为2020年7月15日
>>> dt_utc
datetime.datetime(2020, 7, 15, 12, 45, 20, tzinfo=<UTC>)

# 在UTC标准时间的基础上增加两周，变成了2020年7月29日，此时时间对象仍然是UTC标准时间对象
>>> dt_delta1 = add_timedelta(dt_utc, timedelta(weeks=2))

# 通过UTC进行时间增量处理，得到的结果是正确的
>>> dt_delta1
datetime.datetime(2020, 7, 29, 12, 45, 20, tzinfo=<UTC>)

# 指定的转换过程中需要使用的时区，会先将dt_utc转换成美国东部时间，再增加两周，再将美国东部时间转换成UTC标准时间，此时转换的结果也是正确的
>>> dt_delta2 = add_timedelta(dt_utc, timedelta(weeks=2), within_tz=eastern)

>>> dt_delta2
datetime.datetime(2020, 7, 29, 12, 45, 20, tzinfo=<UTC>)

# 将中国本地时间加两周，最终的结果也是对的
>>> dt_delta3 = add_timedelta(dt_local, timedelta(weeks=2), within_tz=eastern)

>>> dt_delta3
datetime.datetime(2020, 7, 29, 20, 45, 20, tzinfo=<DstTzInfo 'Asia/Shanghai' CST+8:00:00 STD>)

>>>

floor向下取最近的时间边界值 ​

fleming.floor(dt, within_tz=None, year=None, month=None, week=None, day=None, hour=None, minute=None, second=None, microsecond=None, extra_td_if_floor=None)向下取最近的时间边界值。将日期时间四舍五入到最接近的时间间隔。 可用的时间间隔是年year，月month，周week，日day，小时hour，分钟minute，秒second和微秒microsecond。

参数中需要注意的是week参数，代码周，只能是1或默认的None，指定week时则year和month参数不生效。

python
$ ipython
Python 3.6.8 (v3.6.8:3c6b436a57, Dec 24 2018, 02:10:22)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.13.0 -- An enhanced Interactive Python. Type '?' for help.

>>> from datetime import datetime

>>> from pytz import timezone, UTC

>>> from fleming import floor, convert_to_tz

>>> today = datetime.today()

# 本地时间，不带时区信息
>>> today
datetime.datetime(2020, 7, 15, 22, 15, 33, 460350)

# 本地时间，带时区信息
>> dt_local = today.replace(tzinfo=timezone('Asia/Shanghai'))

# 本地时间，带时区信息
>>> dt_local
datetime.datetime(2020, 7, 15, 22, 15, 33, 460350, tzinfo=<DstTzInfo 'Asia/Shanghai' LMT+8:06:00 STD>)

# 将本地时间转换成UTC标准时间
>>> dt_utc = convert_to_tz(dt_local, UTC)

# UTC标准时间
>>> dt_utc
datetime.datetime(2020, 7, 15, 14, 9, 33, 460350, tzinfo=<UTC>)

>>>

对无时区时间对象进行处理 ​

对年和月间隔的处理：

python
>>> floor(today, year=1)
datetime.datetime(2020, 1, 1, 0, 0)

>>> floor(today, year=2)
datetime.datetime(2020, 1, 1, 0, 0)

>>> floor(today, year=3)
datetime.datetime(2019, 1, 1, 0, 0)

>>> floor(today, year=4)
datetime.datetime(2020, 1, 1, 0, 0)

>>> floor(today, year=5)
datetime.datetime(2020, 1, 1, 0, 0)

>>> floor(today, year=6)
datetime.datetime(2016, 1, 1, 0, 0)

>>> floor(today, year=7)
datetime.datetime(2016, 1, 1, 0, 0)

>>> floor(today, year=8)
datetime.datetime(2016, 1, 1, 0, 0)

>>> floor(today, year=9)
datetime.datetime(2016, 1, 1, 0, 0)

>>> floor(today, year=10)
datetime.datetime(2020, 1, 1, 0, 0)

>>> floor(today, year=11)
datetime.datetime(2013, 1, 1, 0, 0)

>>> for i in range(1, 50):
...     print('floor(today,year=%s) = %s' % (i, floor(today, year=i)))
...
floor(today,year=1) = 2020-01-01 00:00:00
floor(today,year=2) = 2020-01-01 00:00:00
floor(today,year=3) = 2019-01-01 00:00:00
floor(today,year=4) = 2020-01-01 00:00:00
floor(today,year=5) = 2020-01-01 00:00:00
floor(today,year=6) = 2016-01-01 00:00:00
floor(today,year=7) = 2016-01-01 00:00:00
floor(today,year=8) = 2016-01-01 00:00:00
floor(today,year=9) = 2016-01-01 00:00:00
floor(today,year=10) = 2020-01-01 00:00:00
floor(today,year=11) = 2013-01-01 00:00:00
floor(today,year=12) = 2016-01-01 00:00:00
floor(today,year=13) = 2015-01-01 00:00:00
floor(today,year=14) = 2016-01-01 00:00:00
floor(today,year=15) = 2010-01-01 00:00:00
floor(today,year=16) = 2016-01-01 00:00:00
floor(today,year=17) = 2006-01-01 00:00:00
floor(today,year=18) = 2016-01-01 00:00:00
floor(today,year=19) = 2014-01-01 00:00:00
floor(today,year=20) = 2020-01-01 00:00:00
floor(today,year=21) = 2016-01-01 00:00:00
floor(today,year=22) = 2002-01-01 00:00:00
floor(today,year=23) = 2001-01-01 00:00:00
floor(today,year=24) = 2016-01-01 00:00:00
floor(today,year=25) = 2000-01-01 00:00:00
floor(today,year=26) = 2002-01-01 00:00:00
floor(today,year=27) = 1998-01-01 00:00:00
floor(today,year=28) = 2016-01-01 00:00:00
floor(today,year=29) = 2001-01-01 00:00:00
floor(today,year=30) = 2010-01-01 00:00:00
floor(today,year=31) = 2015-01-01 00:00:00
floor(today,year=32) = 2016-01-01 00:00:00
floor(today,year=33) = 2013-01-01 00:00:00
floor(today,year=34) = 2006-01-01 00:00:00
floor(today,year=35) = 1995-01-01 00:00:00
floor(today,year=36) = 2016-01-01 00:00:00
floor(today,year=37) = 1998-01-01 00:00:00
floor(today,year=38) = 2014-01-01 00:00:00
floor(today,year=39) = 1989-01-01 00:00:00
floor(today,year=40) = 2000-01-01 00:00:00
floor(today,year=41) = 2009-01-01 00:00:00
floor(today,year=42) = 2016-01-01 00:00:00
floor(today,year=43) = 1978-01-01 00:00:00
floor(today,year=44) = 1980-01-01 00:00:00
floor(today,year=45) = 1980-01-01 00:00:00
floor(today,year=46) = 1978-01-01 00:00:00
floor(today,year=47) = 1974-01-01 00:00:00
floor(today,year=48) = 2016-01-01 00:00:00
floor(today,year=49) = 2009-01-01 00:00:00

>>>

你发现的什么规律吗？当year值从1到49时，floor得到的值有什么规律？我完成没有发现，输出完成与预期不一样！😢 搞不懂！我猜后面的ceil方法也可能有类似的问题，我决定放弃这个模块的学习。Bye! 你如果想再深入的学习，可参考https://fleming.readthedocs.io/en/develop/index.html。

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