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Tables and GeoParquet

egms_client.tables

The CSVs of the archive as tables. Needs the tables extra.

Every ZIP holds a CSV with one row per measurement point (L2A, L2B) or per 100 m cell (L3): its attributes, then one column per acquisition date (20251119) with the displacement in millimetres since the first date.

  • L2 columns start pid, mp_type, latitude, longitude, easting, northing, height_ortho, ...; mean_velocity is along the satellite's line of sight (los_east, los_north, los_up give its direction);
  • L3 columns start pid, easting, northing, height_ortho, ...; mean_velocity is vertical (U) or east-west (E).

easting / northing are EPSG:3035 metres in both, so :func:to_geoparquet writes one geometry for every level. Values are mm and mm/year. An L3 CSV is ~250 MB for ~170,000 cells and ~300 dates.

read_csv 

read_csv(path: Path, *, series: bool = True, columns: Sequence[str] | None = None) -> Any

A CSV of the archive as a pandas.DataFrame.

series=False skips the date columns (much less memory); columns keeps only those attributes (pid is always kept). Dates are float32.

dates 

dates(frame: Any) -> list[date]

The acquisition dates of a table, in its column order.

attributes 

attributes(frame: Any) -> Any

The table without its date columns.

series_long 

series_long(frame: Any) -> Any

One row per point and date: pid, date, displacement_mm.

to_xarray 

to_xarray(frame: Any) -> Any

The series of a table as an xarray.DataArray over (pid, time).

Values are the displacement in mm (float32). Every attribute column (easting, mean_velocity, ...) becomes a coordinate along pid, so points can be selected by them. Needs xarray.

to_geoparquet 

to_geoparquet(frame: Any, path: Path) -> Path

Write a table as GeoParquet: points in EPSG:3035 from easting / northing.

Plain Parquet readers see the columns; GIS tools (GeoPandas, QGIS, DuckDB spatial) also see the geometry. Written to .part first.

csv_to_geoparquet 

csv_to_geoparquet(csv: Path, parquet: Path | None = None, *, series: bool = True) -> Path

A CSV of the archive straight to GeoParquet, next to it by default.

Streamed in blocks with pyarrow alone, so memory stays flat however large the CSV: an L3 CSV of 250 MB becomes ~60 MB of Parquet. The file carries no bbox (optional in GeoParquet), as it is not known before the end.