Hi, After several weeks of gathering and pondering through very valuable feedback, we are happy to release the third (and final?) version of the proposal for the addition of the date/time types in NumPy. The bad news is that, due to a series of circumstances (apparently not related on how this job was being done) Enthought is no longer funding this project. However, we decided to go ahead and publish this in the hope that it could be useful in the future for someone brave enough to willing to do an implementation. So, while I'll be glad to answer the doubts or questions about this proposal, it should be clear that we (Ivan Vilata and myself) are not going to proceed with the implementation phase (unless Enthought would find fundings for sponsoring this in the future). Finally, I'd like to thank everybody that has been involved in the making of this proposal. Without their advices, suggestions and criticism we probably have never been able to realize all the requeriments and intrincacies that need a dtype for allowing a date/time manipulation which is adequate for numerical purposes. Cheers, -- Francesc Alted ==================================================================== A (third) proposal for implementing some date/time types in NumPy ==================================================================== :Author: Francesc Alted i Abad :Contact: faltet@pytables.com :Author: Ivan Vilata i Balaguer :Contact: ivan@selidor.net :Date: 2008-07-30 Executive summary ================= A date/time mark is something very handy to have in many fields where one has to deal with data sets. While Python has several modules that define a date/time type (like the integrated ``datetime`` [1]_ or ``mx.DateTime`` [2]_), NumPy has a lack of them. In this document, we are proposing the addition of a series of date/time types to fill this gap. The requirements for the proposed types are two-folded: 1) they have to be fast to operate with and 2) they have to be as compatible as possible with the existing ``datetime`` module that comes with Python. Types proposed ============== To start with, it is virtually impossible to come up with a single date/time type that fills the needs of every case of use. So, after pondering about different possibilities, we have stuck with *two* different types, namely ``datetime64`` and ``timedelta64`` (these names are preliminary and can be changed), that can have different time units so as to cover different needs. .. Important:: the time unit is conceived here as metadata that *complements* a date/time dtype, *without changing the base type*. It provides information about the *meaning* of the stored numbers, not about their *structure*. Now follows a detailed description of the proposed types. ``datetime64`` -------------- It represents a time that is absolute (i.e. not relative). It is implemented internally as an ``int64`` type. The internal epoch is the POSIX epoch (see [3]_). Like POSIX, the representation of a date doesn't take leap seconds into account. In time unit *conversions* and time *representations* (but not in other time computations), the value -2**63 (0x8000000000000000) is interpreted as an invalid or unknown date, *Not a Time* or *NaT*. See the section on time unit conversions for more information. Time units ~~~~~~~~~~ It accepts different time units, each of them implying a different time span. The table below describes the time units supported with their corresponding time spans. ======== ================ ========================== Time unit Time span (years) ------------------------- -------------------------- Code Meaning ======== ================ ========================== Y year [9.2e18 BC, 9.2e18 AC] M month [7.6e17 BC, 7.6e17 AC] W week [1.7e17 BC, 1.7e17 AC] B business day [3.5e16 BC, 3.5e16 AC] D day [2.5e16 BC, 2.5e16 AC] h hour [1.0e15 BC, 1.0e15 AC] m minute [1.7e13 BC, 1.7e13 AC] s second [ 2.9e9 BC, 2.9e9 AC] ms millisecond [ 2.9e6 BC, 2.9e6 AC] us microsecond [290301 BC, 294241 AC] ns nanosecond [ 1678 AC, 2262 AC] ======== ================ ========================== The value of an absolute date is thus *an integer number of units of the chosen time unit* passed since the internal epoch. When working with business days, Saturdays and Sundays are simply ignored from the count (i.e. day 3 in business days is not Saturday 1970-01-03, but Monday 1970-01-05). Building a ``datetime64`` dtype ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The proposed ways to specify the time unit in the dtype constructor are: Using the long string notation:: dtype('datetime64[us]') Using the short string notation:: dtype('T8[us]') Note that a time unit should always be specified, as there is not a default. Setting and getting values ~~~~~~~~~~~~~~~~~~~~~~~~~~ The objects with this dtype can be set in a series of ways:: t = numpy.ones(3, dtype='T8[s]') t[0] = 1199164176 # assign to July 30th, 2008 at 17:31:00 t[1] = datetime.datetime(2008, 7, 30, 17, 31, 01) # with datetime module t[2] = '2008-07-30T17:31:02' # with ISO 8601 And can be get in different ways too:: str(t[0]) --> 2008-07-30T17:31:00 repr(t[1]) --> datetime64(1199164177, 's') str(t[0].item()) --> 2008-07-30 17:31:00 # datetime module object repr(t[0].item()) --> datetime.datetime(2008, 7, 30, 17, 31) # idem str(t) --> [2008-07-30T17:31:00 2008-07-30T17:31:01 2008-07-30T17:31:02] repr(t) --> array([1199164176, 1199164177, 1199164178], dtype='datetime64[s]') Comparisons ~~~~~~~~~~~ The comparisons will be supported too:: numpy.array(['1980'], 'T8[Y]') == numpy.array(['1979'], 'T8[Y]') --> [False] or by applying broadcasting:: numpy.array(['1979', '1980'], 'T8[Y]') == numpy.datetime64 ('1980', 'Y') --> [False, True] The next should work too:: numpy.array(['1979', '1980'], 'T8[Y]') == '1980-01-01' --> [False, True] because the right hand expression can be broadcasted into an array of 2 elements of dtype 'T8[Y]'. Compatibility issues ~~~~~~~~~~~~~~~~~~~~ This will be fully compatible with the ``datetime`` class of the ``datetime`` module of Python only when using a time unit of microseconds. For other time units, the conversion process will loose precision or will overflow as needed. The conversion from/to a ``datetime`` object doesn't take leap seconds into account. ``timedelta64`` --------------- It represents a time that is relative (i.e. not absolute). It is implemented internally as an ``int64`` type. In time unit *conversions* and time *representations* (but not in other time computations), the value -2**63 (0x8000000000000000) is interpreted as an invalid or unknown time, *Not a Time* or *NaT*. See the section on time unit conversions for more information. Time units ~~~~~~~~~~ It accepts different time units, each of them implying a different time span. The table below describes the time units supported with their corresponding time spans. ======== ================ ========================== Time unit Time span ------------------------- -------------------------- Code Meaning ======== ================ ========================== Y year +- 9.2e18 years M month +- 7.6e17 years W week +- 1.7e17 years B business day +- 3.5e16 years D day +- 2.5e16 years h hour +- 1.0e15 years m minute +- 1.7e13 years s second +- 2.9e12 years ms millisecond +- 2.9e9 years us microsecond +- 2.9e6 years ns nanosecond +- 292 years ps picosecond +- 106 days fs femtosecond +- 2.6 hours as attosecond +- 9.2 seconds ======== ================ ========================== The value of a time delta is thus *an integer number of units of the chosen time unit*. Building a ``timedelta64`` dtype ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The proposed ways to specify the time unit in the dtype constructor are: Using the long string notation:: dtype('timedelta64[us]') Using the short string notation:: dtype('t8[us]') Note that a time unit should always be specified, as there is not a default. Setting and getting values ~~~~~~~~~~~~~~~~~~~~~~~~~~ The objects with this dtype can be set in a series of ways:: t = numpy.ones(3, dtype='t8[ms]') t[0] = 12 # assign to 12 ms t[1] = datetime.timedelta(0, 0, 13000) # 13 ms t[2] = '0:00:00.014' # 14 ms And can be get in different ways too:: str(t[0]) --> 0:00:00.012 repr(t[1]) --> timedelta64(13, 'ms') str(t[0].item()) --> 0:00:00.012000 # datetime module object repr(t[0].item()) --> datetime.timedelta(0, 0, 12000) # idem str(t) --> [0:00:00.012 0:00:00.014 0:00:00.014] repr(t) --> array([12, 13, 14], dtype="timedelta64[ms]") Comparisons ~~~~~~~~~~~ The comparisons will be supported too:: numpy.array([12, 13, 14], 't8[ms]') == numpy.array([12, 13, 13], 't8 [ms]') --> [True, True, False] or by applying broadcasting:: numpy.array([12, 13, 14], 't8[ms]') == numpy.timedelta64(13, 'ms') --> [False, True, False] The next should work too:: numpy.array([12, 13, 14], 't8[ms]') == '0:00:00.012' --> [True, False, False] because the right hand expression can be broadcasted into an array of 3 elements of dtype 't8[ms]'. Compatibility issues ~~~~~~~~~~~~~~~~~~~~ This will be fully compatible with the ``timedelta`` class of the ``datetime`` module of Python only when using a time unit of microseconds. For other units, the conversion process will loose precision or will overflow as needed. Examples of use =============== Here it is an example of use for the ``datetime64``:: In [5]: numpy.datetime64(42, 'us') Out[5]: datetime64(42, 'us') In [6]: print numpy.datetime64(42, 'us') 1970-01-01T00:00:00.000042 # representation in ISO 8601 format In [7]: print numpy.datetime64(367.7, 'D') # decimal part is lost 1971-01-02 # still ISO 8601 format In [8]: numpy.datetime('2008-07-18T12:23:18', 'm') # from ISO 8601 Out[8]: datetime64(20273063, 'm') In [9]: print numpy.datetime('2008-07-18T12:23:18', 'm') Out[9]: 2008-07-18T12:23 In [10]: t = numpy.zeros(5, dtype="datetime64[ms]") In [11]: t[0] = datetime.datetime.now() # setter in action In [12]: print t [2008-07-16T13:39:25.315 1970-01-01T00:00:00.000 1970-01-01T00:00:00.000 1970-01-01T00:00:00.000 1970-01-01T00:00:00.000] In [13]: repr(t) Out[13]: array([267859210457, 0, 0, 0, 0], dtype="datetime64[ms]") In [14]: t[0].item() # getter in action Out[14]: datetime.datetime(2008, 7, 16, 13, 39, 25, 315000) In [15]: print t.dtype dtype('datetime64[ms]') And here it goes an example of use for the ``timedelta64``:: In [5]: numpy.timedelta64(10, 'us') Out[5]: timedelta64(10, 'us') In [6]: print numpy.timedelta64(10, 'us') 0:00:00.000010 In [7]: print numpy.timedelta64(3600.2, 'm') # decimal part is lost 2 days, 12:00 In [8]: t1 = numpy.zeros(5, dtype="datetime64[ms]") In [9]: t2 = numpy.ones(5, dtype="datetime64[ms]") In [10]: t = t2 - t1 In [11]: t[0] = datetime.timedelta(0, 24) # setter in action In [12]: print t [0:00:24.000 0:00:01.000 0:00:01.000 0:00:01.000 0:00:01.000] In [13]: print repr(t) Out[13]: array([24000, 1, 1, 1, 1], dtype="timedelta64[ms]") In [14]: t[0].item() # getter in action Out[14]: datetime.timedelta(0, 24) In [15]: print t.dtype dtype('timedelta64[s]') Operating with date/time arrays =============================== ``datetime64`` vs ``datetime64`` -------------------------------- The only arithmetic operation allowed between absolute dates is the subtraction:: In [10]: numpy.ones(3, "T8[s]") - numpy.zeros(3, "T8[s]") Out[10]: array([1, 1, 1], dtype=timedelta64[s]) But not other operations:: In [11]: numpy.ones(3, "T8[s]") + numpy.zeros(3, "T8[s]") TypeError: unsupported operand type(s) for +: 'numpy.ndarray' and 'numpy.ndarray' Comparisons between absolute dates are allowed. Casting rules ~~~~~~~~~~~~~ When operating (basically, only the subtraction will be allowed) two absolute times with different unit times, the outcome would be to raise an exception. This is because the ranges and time-spans of the different time units can be very different, and it is not clear at all what time unit will be preferred for the user. For example, this should be allowed::
numpy.ones(3, dtype="T8[Y]") - numpy.zeros(3, dtype="T8[Y]") array([1, 1, 1], dtype="timedelta64[Y]")
But the next should not::
numpy.ones(3, dtype="T8[Y]") - numpy.zeros(3, dtype="T8[ns]") raise numpy.IncompatibleUnitError # what unit to choose?
series = numpy.array(['1970-01-01', '1970-02-01', '1970-09-01'],
series2 = series + numpy.timedelta(1, 'Y') # Add 2 relative years series2 array(['1972-01-01', '1972-02-01', '1972-09-01'],
``datetime64`` vs ``timedelta64`` --------------------------------- It will be possible to add and subtract relative times from absolute dates:: In [10]: numpy.zeros(5, "T8[Y]") + numpy.ones(5, "t8[Y]") Out[10]: array([1971, 1971, 1971, 1971, 1971], dtype=datetime64[Y]) In [11]: numpy.ones(5, "T8[Y]") - 2 * numpy.ones(5, "t8[Y]") Out[11]: array([1969, 1969, 1969, 1969, 1969], dtype=datetime64[Y]) But not other operations:: In [12]: numpy.ones(5, "T8[Y]") * numpy.ones(5, "t8[Y]") TypeError: unsupported operand type(s) for *: 'numpy.ndarray' and 'numpy.ndarray' Casting rules ~~~~~~~~~~~~~ In this case the absolute time should have priority for determining the time unit of the outcome. That would represent what the people wants to do most of the times. For example, this would allow to do:: dtype='datetime64[D]') dtype='datetime64[D]') # the 'D'ay time unit has been chosen ``timedelta64`` vs ``timedelta64`` ---------------------------------- Finally, it will be possible to operate with relative times as if they were regular int64 dtypes *as long as* the result can be converted back into a ``timedelta64``:: In [10]: numpy.ones(3, 't8[us]') Out[10]: array([1, 1, 1], dtype="timedelta64[us]") In [11]: (numpy.ones(3, 't8[M]') + 2) ** 3 Out[11]: array([27, 27, 27], dtype="timedelta64[M]") But:: In [12]: numpy.ones(5, 't8') + 1j TypeError: the result cannot be converted into a ``timedelta64`` Casting rules ~~~~~~~~~~~~~ When combining two ``timedelta64`` dtypes with different time units the outcome will be the shorter of both ("keep the precision" rule). For example:: In [10]: numpy.ones(3, 't8[s]') + numpy.ones(3, 't8[m]') Out[10]: array([61, 61, 61], dtype="timedelta64[s]") However, due to the impossibility to know the exact duration of a relative year or a relative month, when these time units appear in one of the operands, the operation will not be allowed:: In [11]: numpy.ones(3, 't8[Y]') + numpy.ones(3, 't8[D]') raise numpy.IncompatibleUnitError # how to convert relative years to days? In order to being able to perform the above operation a new NumPy function, called ``change_timeunit`` is proposed. Its signature will be:: change_timeunit(time_object, new_unit, reference) where 'time_object' is the time object whose unit is to be changed, 'new_unit' is the desired new time unit, and 'reference' is an absolute date (NumPy datetime64 scalar) that will be used to allow the conversion of relative times in case of using time units with an uncertain number of smaller time units (relative years or months cannot be expressed in days). With this, the above operation can be done as follows:: In [10]: t_years = numpy.ones(3, 't8[Y]') In [11]: t_days = numpy.change_timeunit(t_years, 'D', '2001-01-01') In [12]: t_days + numpy.ones(3, 't8[D]') Out[12]: array([366, 366, 366], dtype="timedelta64[D]") dtype vs time units conversions =============================== For changing the date/time dtype of an existing array, we propose to use the ``.astype()`` method. This will be mainly useful for changing time units. For example, for absolute dates:: In[10]: t1 = numpy.zeros(5, dtype="datetime64[s]") In[11]: print t1 [1970-01-01T00:00:00 1970-01-01T00:00:00 1970-01-01T00:00:00 1970-01-01T00:00:00 1970-01-01T00:00:00] In[12]: print t1.astype('datetime64[D]') [1970-01-01 1970-01-01 1970-01-01 1970-01-01 1970-01-01] For relative times:: In[10]: t1 = numpy.ones(5, dtype="timedelta64[s]") In[11]: print t1 [1 1 1 1 1] In[12]: print t1.astype('timedelta64[ms]') [1000 1000 1000 1000 1000] Changing directly from/to relative to/from absolute dtypes will not be supported:: In[13]: numpy.zeros(5, dtype="datetime64[s]").astype('timedelta64') TypeError: data type cannot be converted to the desired type Business days have the peculiarity that they do not cover a continuous line of time (they have gaps at weekends). Thus, when converting from any ordinary time to business days, it can happen that the original time is not representable. In that case, the result of the conversion is *Not a Time* (*NaT*):: In[10]: t1 = numpy.arange(5, dtype="datetime64[D]") In[11]: print t1 [1970-01-01 1970-01-02 1970-01-03 1970-01-04 1970-01-05] In[12]: t2 = t1.astype("datetime64[B]") In[13]: print t2 # 1970 begins in a Thursday [1970-01-01 1970-01-02 NaT NaT 1970-01-05] When converting back to ordinary days, NaT values are left untouched (this happens in all time unit conversions):: In[14]: t3 = t2.astype("datetime64[D]") In[13]: print t3 [1970-01-01 1970-01-02 NaT NaT 1970-01-05] Final considerations ==================== Why the ``origin`` metadata disappeared --------------------------------------- During the discussion of the date/time dtypes in the NumPy list, the idea of having an ``origin`` metadata that complemented the definition of the absolute ``datetime64`` was initially found to be useful. However, after thinking more about this, we found that the combination of an absolute ``datetime64`` with a relative ``timedelta64`` does offer the same functionality while removing the need for the additional ``origin`` metadata. This is why we have removed it from this proposal. Operations with mixed time units -------------------------------- Whenever an operation between two time values of the same dtype with the same unit is accepted, the same operation with time values of different units should be possible (e.g. adding a time delta in seconds and one in microseconds), resulting in an adequate time unit. The exact semantics of this kind of operations is defined int the "Casting rules" subsections of the "Operating with date/time arrays" section. Due to the peculiarities of business days, it is most probable that operations mixing business days with other time units will not be allowed. Why there is not a ``quarter`` time unit? ----------------------------------------- This proposal tries to focus on the most common used set of time units to operate with, and the ``quarter`` can be considered more of a derived unit. Besides, the use of a ``quarter`` normally requires that it can start at whatever month of the year, and as we are not including support for a time ``origin`` metadata, this is not a viable venue here. Finally, if we were to add the ``quarter`` then people should expect to find a ``biweekly``, ``semester`` or ``biyearly`` just to put some examples of other derived units, and we find this a bit too overwhelming for this proposal purposes. .. [1] http://docs.python.org/lib/module-datetime.html .. [2] http://www.egenix.com/products/python/mxBase/mxDateTime .. [3] http://en.wikipedia.org/wiki/Unix_time .. Local Variables: .. mode: rst .. coding: utf-8 .. fill-column: 72 .. End: