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SegmentTable: Basics

For a more comprehensive guide including visualization, GravitySpy integration, and advanced operations, see the Table / Segment User Guide.

GWpy base classes and gwexpy extensions

Segment analysis builds on GWpy types. Each row span is still represented by gwpy.segments.Segment, and payload columns can hold GWpy TimeSeries or FrequencySeries objects directly.

On top of that, gwexpy adds SegmentTable as an extension of gwpy.table.Table, plus lazy payload handling via SegmentCell, and table-oriented batch helpers such as apply(), map(), crop(), and asd(). In practice, you keep GWpy base classes for the actual data objects while gwexpy adds the workflow layer for processing many segments together.

import warnings

warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning)

import warnings

with warnings.catch_warnings():
    warnings.simplefilter('ignore')

    import numpy as np
    from gwpy.segments import Segment

    from gwexpy.table import SegmentTable

    # 1. Create simple segments
    segs = [Segment(0, 4), Segment(4, 8), Segment(8, 12)]
    st = SegmentTable.from_segments(segs, label=["A", "B", "C"])
    st

Delayed Loading with SegmentCell

You can add payload columns that are only loaded when needed. This is useful for handling large datasets.

def my_loader():
    # Simulate loading data
    print("Loading series...")
    from gwpy.timeseries import TimeSeries
    return TimeSeries(np.random.randn(128), sample_rate=32)

# Add a payload column with a loader (sequence of callables)
st.add_series_column("raw", loader=[my_loader]*len(st), kind="timeseries")
st

	span	label	raw
0	(0, 4)	A	<lazy: timeseries>
1	(4, 8)	B	<lazy: timeseries>
2	(8, 12)	C	<lazy: timeseries>

Row-wise Processing

SegmentTable provides an apply() method to process each row and collect results into new columns.

def process_row(row):
    span = row["span"]
    return {"duration": float(span[1] - span[0]), "valid": True}

st2 = st.apply(process_row)
st2.display()

	span	label	duration	valid	raw
0	(0, 4)	A	4.0	True	<lazy: timeseries>
1	(4, 8)	B	4.0	True	<lazy: timeseries>
2	(8, 12)	C	4.0	True	<lazy: timeseries>

Fetch and Conversion

You can explicitly load lazy cells with fetch() or materialize(). Converting to pandas gives you a standard DataFrame for meta columns.

st2.fetch()
df = st2.to_pandas()
df.head()

Loading series...
Loading series...
Loading series...

	span	label	duration	valid
0	(0, 4)	A	4.0	True
1	(4, 8)	B	4.0	True
2	(8, 12)	C	4.0	True