raddb 0.0.1

Generic radar data archiving library — archive xarray DataTree volumes as Parquet

RadDB — generic radar data archiving & analysis

RadDB archives xarray DataTree radar volumes as compact Parquet files and gives you a small, fluent object API to load, filter, crop, cross-section and plot them. It is network-agnostic: any DataTree with the standard xradar coordinate layout (MeteoSwiss, NEXRAD, …) can be archived and analysed — no pyart required.

MeteoSwiss/METRANET-specific ingestion (pyart + radar_api) lives in the private raddb.mch subpackage and is kept out of the generic core.

Storage model

A radar is stored as a static LUT (per-gate geometry, generated once) plus one POL parquet per volume (the dynamic moments), linked by an integer gate_id:

{archive_dir}/{radar}/LUT/{radar}_LUT.parquet          # gate_id, lat/lon/alt, x_<epsg>/y_<epsg>, sweep, …
{archive_dir}/{radar}/{YYYY}/{MM}/{DD}/{radar}_{YYYYMMDD}_{HHMMSS}_POL.parquet

No-echo gates are dropped at archive time (default DBZH > 0), so the archive stays small.

Installation

pip install -e .            # core
pip install -e ".[viz]"     # + cartopy / pyproj / shapely for maps

Core runtime deps: numpy, pandas, polars, geopandas, xarray, pyarrow, dask, fsspec, s3fs, matplotlib.

Quick start

RadDB is a single, dual-role class:

• archive-bound — db = RadDB(archive_dir, crs=…); use it to archive() and open().
• data-carrying — the object returned by open() (call it rdf). It holds the data as a polars DataFrame (rdf.data) and exposes the query / convert / crop / cross-section / plot methods. Each of those returns a new RadDB, so calls chain fluently.

import raddb

db = raddb.RadDB(archive_dir="/data/raddb", crs=2056)   # 2056 = CH1903+/LV95

# --- archive -----------------------------------------------------------------
# From saved DataTree files on disk (.zarr / .nc); the LUT is auto-generated:
db.archive(datatree_dir="/data/MCH_datatree")           # radar inferred per file
# ...or archive in-memory DataTrees directly:
# db.archive(datatree=dt, radar="A")
# db.archive(datatree=[dt1, dt2], radar="A")
# db.archive(datatree={"A": [dt1], "D": [dt2]})         # multi-radar

# --- open --------------------------------------------------------------------
rdf = db.open(time_period=("2024-08-26", "2024-08-27"), radars="L")
print(rdf)                     # rich summary: gates, radars, time range, columns
len(rdf), rdf.columns(), rdf.radars()
rdf.start_time(), rdf.end_time()
rdf.extent()                   # [xmin, xmax, ymin, ymax] in `crs`
rdf.geographic_extent()        # [lon_min, lon_max, lat_min, lat_max]
rdf.crs(), rdf.geographic_crs()

# --- filter / convert --------------------------------------------------------
strong = rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20})
strong = rdf.filter([{"var": "DBZH", "logic": ">", "threshold": 20},
                     {"var": "RHOHV", "logic": ">", "threshold": 0.9}])   # AND
pdf = rdf.to_pandas(with_geometry=True)     # pandas + gate coordinates
gdf = rdf.to_geopandas()                    # GeoDataFrame (with CRS)
dt  = rdf.to_datatree()                     # xarray DataTree (for plotting)

# --- area of interest --------------------------------------------------------
box   = rdf.crop_by_bbox(extent=[2.60e6, 2.62e6, 1.11e6, 1.13e6])   # or bounds=(xmin,ymin,xmax,ymax)
poly  = rdf.crop_by_polygone("catchment.geojson")
disc  = rdf.crop_around_point((2.61e6, 1.12e6), distance=20_000)    # metres
# rdf.interactive_crop()      # draw an AOI on a Jupyter map (needs ipyleaflet)

# --- cross-section -----------------------------------------------------------
cs = rdf.extract_cross_section(p1=(2.60e6, 1.12e6), p2=(2.63e6, 1.12e6))

# --- plot --------------------------------------------------------------------
rdf.plot_ppi(sweep=1, variable="DBZH", save="ppi.png")
cs.plot_cross_section(variable="DBZH", save="xsec.png")
rdf.plot_rhi(azimuth=270, variable="DBZH")

# fluent: open -> filter -> crop -> plot
rdf.filter({"var": "DBZH", "logic": ">", "threshold": 20}) \
   .crop_by_bbox(extent=rdf.extent()) \
   .plot_ppi(variable="DBZH", save="strong.png")

filter / crs argument shapes: filters are {"var", "logic", "threshold"} dicts (logic ∈ ==,!=,>,>=,<,<=); crs is an EPSG int (e.g. 2056), a CRS-coercible object, or None.

What is on disk? (archive-bound)

db.inventory()                      # radars, volume counts, time ranges, size
db.inventory(detailed=True)         # + LUT info, stored moments, day-by-day counts
db.inventory(datatree_dir="/data/MCH_datatree")   # DataTree files not archived yet

LUT accessors (archive-bound)

db.list_radars()                    # radars present in the archive
db.get_lut("L")                     # the static LUT (pandas)
db.get_radar_info("L")              # site location / sweep geometry
db.add_lut_projection("L", epsg=2056)

Module structure

raddb/
├── __init__.py     # public API surface
├── main.py         # the RadDB class (this API)
├── io_core.py      # DataTree <-> DataFrame <-> Parquet + archive backends
├── lut.py          # LUT generation / geo projection
├── aoi.py          # AOI / crop / cross-section geometry
├── discovery.py    # find_datatree_files + filename-time parsing
├── helper.py       # filters, radar-name normalisation, timers
├── viz/            # plot.py (PPI/RHI/cross-section), interactive.py
└── mch/            # private MeteoSwiss/METRANET ingestion (pyart) — not in the public wheel

Sample-data scripts live under scripts/ (make_sample_mch_datatrees.py, download_nexrad_datatree.py).

Notes

• Projected coordinates / crs. Generating a LUT with a projection (e.g. crs=2056) and the projected accessors (extent, to_geopandas) use pyproj, which needs the PROJ database. A PROJ_DATA / PROJ_LIB inherited from another environment (a conda base env, a system PROJ) points at a proj.db of the wrong PROJ version and makes every projection fail with "no database context specified" — import raddb detects that and repoints PROJ at the running interpreter's own share/proj (see raddb/_proj.py; raddb.PROJ_DATA reports what it changed, None when nothing had to be).
• Zarr / NetCDF. Saved volumes are read back with raddb.open_any_datatree (engine auto-detected); either format works.

License

See LICENSE (MIT).