bw_temporalis.convolution

Attributes

OFFSET

datetime_type

time_types

timedelta_type

Functions

consolidate(→ tuple[numpy.typing.NDArray[numpy.int64], ...)

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

convolve(→ tuple[numpy.typing.NDArray, ...)

temporal_convolution_datetime_timedelta(...)

temporal_convolution_timedelta_timedelta(...)

Module Contents

bw_temporalis.convolution.consolidate(*, indices: numpy.typing.NDArray[numpy.int64], amounts: numpy.typing.NDArray[numpy.float64]) → tuple[numpy.typing.NDArray[numpy.int64], numpy.typing.NDArray[numpy.float64]][source]

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

bw_temporalis.convolution.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]][source]
bw_temporalis.convolution.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]][source]
bw_temporalis.convolution.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]][source]
bw_temporalis.convolution.OFFSET = 31536000000000[source]
bw_temporalis.convolution.datetime_type[source]
bw_temporalis.convolution.time_types[source]
bw_temporalis.convolution.timedelta_type[source]