tspy.data_structures.stream_multi_time_series.SegmentStreamMultiTimeSeries module

class tspy.data_structures.stream_multi_time_series.SegmentStreamMultiTimeSeries.SegmentStreamMultiTimeSeries(tsc, j_stream_mts, trs=None)

Bases: tspy.data_structures.stream_multi_time_series.StreamMultiTimeSeries.StreamMultiTimeSeries

A special form of stream-multi-time-series that consists of observations with a value of type Segment

Methods

add_sink(multi_data_sink)	add a multi-data-sink to this piece of the streaming pipeline.
fillna(interpolator[, null_value])	produce a new stream-multi-time-series which is the result of filling all null values.
filter(func)	produce a new stream-multi-time-series which is the result of filtering by each observation’s value given a filter function.
flatmap(func)	produce a new stream-multi-time-series where each observation’s value in this stream-multi-time-series is mapped to 0 to N new values.
full_join(right_stream_mts[, join_func, …])	join two stream-multi-time-series based on a temporal full join strategy and optionally interpolate missing values
inner_join(right_stream_mts[, join_func])	join two stream-multi-time-series based on a temporal inner join strategy
interval_join(right_stream_mts, filter_func, …)	join two stream-multi-time-series where observations in the right stream lie within an interval of this stream.
left_join(right_stream_mts[, join_func, …])	join two stream-multi-time-series based on a temporal left join strategy and optionally interpolate missing values
left_outer_join(right_stream_mts[, …])	join two stream-multi-time-series based on a temporal left outer join strategy and optionally interpolate missing values
map(func)	produce a new stream-multi-time-series where each observation’s value in this stream-time-series is mapped to a new observation value
map_with_key(func)	produce a new stream-multi-time-series where each observation’s value in this stream-time-series is mapped given a key and value function to a new observation value
partition([num_partitions, …])	partition this stream-multi-time-series such that all partitions can be run on separate threads in parallel
peek()	Optionally get the most recent values in the queue without flushing the queue.
poll([polling_interval])	Get the most recent values in the queue.
resample(periodicity, interpolator)	produce a new stream-muliti-time-series by resampling the current stream-multi-time-series to a given periodicity
right_join(right_stream_mts[, join_func, …])	join two stream-multi-time-series based on a temporal right join strategy and optionally interpolate missing values
right_outer_join(right_stream_mts[, …])	join two stream-multi-time-series based on a temporal right outer join strategy and optionally interpolate missing values
run()	run the streaming pipeline
segment(window[, step])	produce a new segment-multi-time-series from a performing a sliding-based segmentation over each time-series.
segment_by_anchor(func, left_delta, right_delta)	produce a new segment-time-series from performing an anchor-based segmentation over each time-series.
segment_by_time(window[, step])	produce a new segment-time-series from a performing a time-based segmentation over each time-series
to_multi_observation_stream()	Returns
transform(reducer)	transform this stream-multi-time-series of segments into a stream-multi-time-series of values
transform_segments(transform)	produce a new segment-stream-multi-time-series where each segment is transformed to a new segment using a unary transform
with_trs([granularity, start_time])	create a new stream-multi-time-series with its timestamps mapped based on a granularity and start_time.

filter(func)

produce a new stream-multi-time-series which is the result of filtering by each observation’s value given a filter function.

Parameters

funcfunc

the filter on observation’s value function

Returns

StreamMultiTimeSeries

a new stream-multi-time-series

Notes

see filter() for usage

transform(reducer)

transform this stream-multi-time-series of segments into a stream-multi-time-series of values

Parameters

reducerreducer transform

a reducer transform as seen in reducers

Returns

StreamMultiTimeSeries

a new stream-multi-time-series

Notes

Because this observation values are of segment type, a reducer will be used (transform from segment to value)

transform_segments(transform)

produce a new segment-stream-multi-time-series where each segment is transformed to a new segment using a unary transform

Parameters

transformUnaryTransform

the transformation to apply on each segment of this segment-stream-multi-time-series

Returns

SegmentStreamMultiTimeSeries

a new segment-stream-multi-time-series