Plotting¶

SGN-TS provides plotting utilities for visualizing SeriesBuffer and TSFrame data. Requires matplotlib (pip install sgn-ts[plot]).

Plot a Buffer¶

import numpy as np
from sgnts.base import SeriesBuffer
from sgnts.plotting import plot_buffer

buf = SeriesBuffer(offset=0, sample_rate=2048, data=np.random.randn(2048))
fig, ax = plot_buffer(buf)

Plot a Frame¶

plot_frame plots all buffers in a TSFrame, with gap regions shaded automatically:

from sgnts.plotting import plot_frame

fig, ax = plot_frame(frame)

Time Units¶

Control the x-axis unit with time_unit:

plot_buffer(buf, time_unit="s")    # Seconds
plot_buffer(buf, time_unit="ms")   # Milliseconds
plot_buffer(buf, time_unit="ns")   # Nanoseconds
plot_buffer(buf, time_unit="gps")  # GPS time (default)

Gap Visualization¶

Gaps are shaded in red by default. Customize with gap_color and gap_alpha, or disable with show_gaps=False:

plot_frame(frame, gap_color="gray", gap_alpha=0.2)
plot_frame(frame, show_gaps=False)

Multi-channel Data¶

For multi-dimensional buffers, select specific channels with channel:

# Plot only channel 0
plot_buffer(buf, channel=0)

# Plot channels 0 and 1
plot_buffer(buf, channel=(0, 1))

Plotting on Existing Axes¶

Pass a matplotlib Axes to overlay data on an existing plot:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
plot_buffer(buf1, ax=ax, label="Signal A")
plot_buffer(buf2, ax=ax, label="Signal B")
ax.legend()

TSPlotSink¶

Use TSPlotSink to plot data directly from a pipeline:

from sgn import Pipeline
from sgnts.sources import FakeSeriesSource
from sgnts.sinks import TSPlotSink

src = FakeSeriesSource(
    name="src",
    source_pad_names=["out"],
    signal_type="sin",
    rate=2048,
    duration=1,
)
snk = TSPlotSink(name="plot")

Pipeline().connect(src, snk).run()