Chaining Operations

table(g) is lazy: it keeps the HDF5 file and granule context around so operations can auto-pull the columns they need. To keep that context across multiple filters and transforms, use Julia's |> pipe syntax and materialize only at the end:

using DataFrames
using Extents
using SpaceAltimetry

g = granule("GLAH14_634_1102_001_0071_0_01_0001.H5")
ext = Extent(X = (-180.0, 0.0), Y = (60.0, 80.0))

df = table(g) |>
    ICESat.SaturationCorrect() |>
    InExtent(ext) |>
    ICESat.Quality() |>
    DataFrame

The intermediate operations above are lazy. SpaceAltimetry first gathers the union of required columns, reads the HDF5 data once, then applies the operations in left-to-right pipe order. DataFrame can be replaced by collect if you want SpaceAltimetry's lightweight Table/PartitionedTable wrappers instead.

The two-argument verbs remain eager:

t = table(g)
t1 = map(ICESat.SaturationCorrect(), t) # materializes
t2 = filter(ICESat.Quality(), t1) # cannot auto-pull missing HDF5 columns

Use the eager filter/map form for a single operation, or after you have already selected/materialized every column needed by later operations.

Mission-specific operations live in exported namespaces so common names remain short without colliding:

Mission	Operations
ICESat	Quality, SaturationCorrect, TopexToWGS84
ICESat2	Quality for ATL03, ATL06, and ATL08
GEDI	Quality for the full L2A composite quality filter; Sensitivity for gt < sensitivity <= 1.0

For example, ICESat2.Quality in filter(ICESat2.Quality(), table(atl06)) keeps the same high-quality subset identified by the ATL06 point method. GEDI.Quality in filter(GEDI.Quality(), table(gedi)) applies the complete algorithm-aware filter used by points(gedi; filtered=true). Pipe GEDI.Sensitivity after it to keep returns above a sensitivity threshold (0.9 by default).