SGN-TS
SGN-TS extends the SGN streaming pipeline framework with time-series data types: precise offset-based timing, uniformly sampled buffers, frame alignment across multiple channels, and a library of signal processing elements.
Installation
pip install sgn-ts
For PyTorch-accelerated operations (resampling, array backends):
pip install sgn-ts[torch]
Quick Example
Generate a sine wave, amplify it, and collect the output:
from sgn import Pipeline
from sgnts.sources import FakeSeriesSource
from sgnts.transforms import Amplify
from sgnts.sinks import TSFrameCollectSink
src = FakeSeriesSource(
name="src",
source_pad_names=["out"],
signal_type="sin",
rate=2048,
duration=2,
)
amp = Amplify(name="amp", factor=3.0)
snk = TSFrameCollectSink(name="snk")
Pipeline().connect(src, amp).connect(amp, snk).run()
for frame in snk.data["out"]:
print(frame.offset, len(frame.buffers))
Documentation
Full documentation is available at docs.ligo.org/greg/sgn-ts.
Related Libraries
4 maintainers
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