Add data-lake Parquet/Arrow consumer (pymeos.io)#84
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Mirrors the MobilityDuck data-lake recipe so files interoperate: a MEOS-WKB payload column, O(1) native-scalar bounding-box sidecar columns for Parquet/Arrow row-group pruning, and a temporal footer byte-identical to MobilityDuck's temporalFooter(). Adds to_arrow / from_arrow / write_temporal / read_temporal, pyarrow as the optional [parquet] extra, a quickstart example and tests.
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Adds
pymeos.io(to_arrow/from_arrow/write_temporal/read_temporal+temporal_footer), a data-lake interchange that mirrors the MobilityDuck consumer recipe so files written by either tool interoperate: the temporal column is stored as an opaque MEOS-WKB payload, O(1) native-scalar bounding-box sidecar columns (<col>_ts_min/ts_maxand per-typex/y/zspatial orvnumeric bounds) let a Parquet/Arrow engine prune row groups from column statistics without decoding the payload, and thetemporalfooter written to the Parquet KV metadata / Arrow schema metadata is byte-identical to MobilityDuck'stemporalFooter(). WKB reconstruction is type-self-describing via the existing factory, so reads need no type hint.pyarrowis added as the optionalparquetextra and is imported lazily, so the base install is unchanged. Includes a quickstart example (Python port of MobilityDuck'sexamples/quickstart/quickstart.sql) and tests covering footer interop, sidecar emission, Parquet round-trip with predicate pushdown, and the numeric-sidecar path; verified end-to-end against libmeos + pyarrow. The columnar MEOS-ARROW encoding (zero-copy nested Arrow via the MEOS Arrow kernel) is intentionally out of scope here and tracked separately, gated on that kernel landing in a pinned libmeos; the footer is forward-compatible with adding it as an additive encoding with a WKB fallback column.