bw_temporalis.convolution
=========================

.. py:module:: bw_temporalis.convolution


Attributes
----------

.. autoapisummary::

   bw_temporalis.convolution.OFFSET
   bw_temporalis.convolution.datetime_type
   bw_temporalis.convolution.time_types
   bw_temporalis.convolution.timedelta_type


Functions
---------

.. autoapisummary::

   bw_temporalis.convolution.consolidate
   bw_temporalis.convolution.convolve
   bw_temporalis.convolution.temporal_convolution_datetime_timedelta
   bw_temporalis.convolution.temporal_convolution_timedelta_timedelta


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

.. py:function:: consolidate(*, indices: numpy.typing.NDArray[numpy.int64], amounts: numpy.typing.NDArray[numpy.float64]) -> tuple[numpy.typing.NDArray[numpy.int64], numpy.typing.NDArray[numpy.float64]]

   Sum all values in ``amount`` which have the same index in ``indices``


.. py:function:: convolve(*, first_date: numpy.typing.NDArray, first_amount: numpy.typing.NDArray[numpy.float64], second_date: numpy.typing.NDArray, second_amount: numpy.typing.NDArray[numpy.float64], return_dtype: numpy.typing.DTypeLike | str) -> tuple[numpy.typing.NDArray, numpy.typing.NDArray[numpy.float64]]

.. py:function:: temporal_convolution_datetime_timedelta(*, first_date: numpy.typing.NDArray[datetime_type], first_amount: numpy.typing.NDArray[numpy.float64], second_date: numpy.typing.NDArray[timedelta_type], second_amount: numpy.typing.NDArray[numpy.float64]) -> tuple[numpy.typing.NDArray[datetime_type], numpy.typing.NDArray[numpy.float64]]

.. py:function:: temporal_convolution_timedelta_timedelta(*, first_date: numpy.typing.NDArray[timedelta_type], first_amount: numpy.typing.NDArray[numpy.float64], second_date: numpy.typing.NDArray[timedelta_type], second_amount: numpy.typing.NDArray[numpy.float64]) -> tuple[numpy.typing.NDArray[timedelta_type], numpy.typing.NDArray[numpy.float64]]

.. py:data:: OFFSET
   :value: 31536000000000


.. py:data:: datetime_type

.. py:data:: time_types

.. py:data:: timedelta_type