bencher.variables.time
======================

.. py:module:: bencher.variables.time


Classes
-------

.. autoapisummary::

   bencher.variables.time.TimeBase
   bencher.variables.time.TimeSnapshot
   bencher.variables.time.TimeEvent


Module Contents
---------------

.. py:class:: TimeBase(objects=None, default=None, instantiate=False, compute_default_fn=None, check_on_set=None, allow_None=None, empty_default=False, **params)

   Bases: :py:obj:`bencher.variables.sweep_base.SweepBase`, :py:obj:`param.Selector`


   A class to capture a time snapshot of benchmark values.  Time is represent as a continuous value i.e a datetime which is converted into a np.datetime64.  To represent time as a discrete value use the TimeEvent class. The distinction is because holoview and plotly code makes different assumptions about discrete vs continuous variables


   .. py:attribute:: __slots__
      :value: ['units', 'samples', 'optimize']



   .. py:method:: values() -> list[str]

      return all the values for a parameter sweep.  If debug is true return a reduced list



.. py:class:: TimeSnapshot(datetime_src: datetime.datetime | str, units: str = 'time', samples: int | None = None, **params)

   Bases: :py:obj:`TimeBase`


   A class to capture a time snapshot of benchmark values.  Time is represent as a continuous value i.e a datetime which is converted into a np.datetime64.  To represent time as a discrete value use the TimeEvent class. The distinction is because holoview and plotly code makes different assumptions about discrete vs continuous variables


   .. py:attribute:: __slots__
      :value: ['units', 'samples', 'optimize']



   .. py:attribute:: units
      :value: 'time'



   .. py:attribute:: optimize
      :value: False



.. py:class:: TimeEvent(time_event: str, units: str = 'event', samples: int | None = None, **params)

   Bases: :py:obj:`TimeBase`


   A class to represent a discrete event in time where the data was captured i.e a series of pull requests.  Here time is discrete and can't be interpolated, to represent time as a continuous value use the TimeSnapshot class.  The distinction is because holoview and plotly code makes different assumptions about discrete vs continuous variables


   .. py:attribute:: __slots__
      :value: ['units', 'samples', 'optimize']



   .. py:attribute:: units
      :value: 'event'



   .. py:attribute:: optimize
      :value: False