rustwx 0.5.11

Rust-first weather model metadata and projected map rendering bindings

rustwx Python bindings

rustwx is the optional Python binding package for the Rust-first rustwx weather workspace.

Design goal

Keep Python convenient, keep the hot path in Rust, and expose generic render/model metadata surfaces that are usable outside WRF-specific callers.

What is implemented

With the python feature enabled, the module exposes:

• agent-facing discovery and map rendering via agent_capabilities_json, list_domains_json, render_maps_json, render_glm_lightning_json, and sample_point_timeseries_json
• model listing and source/model helpers
• projected-grid rendering via render_projected_map and render_projected_map_json
• compatibility aliases render_wrf_map and render_wrf_map_json
• standalone projected projection metadata via describe_projected_projection
• standalone projected grid/layout metadata via describe_projected_geometry
• standalone projected CONUS basemap overlay extraction via build_projected_basemap_overlays
• future-facing cross-section request validation/normalization via normalize_cross_section_request
• native sounding-column rendering via render_sounding_column and render_sounding_column_json

The wheel also installs a stable rustwx console command for agent and MCP adapters, plus a standalone local web UI command:

rustwx capabilities
rustwx list-domains --kind country --limit 5
rustwx prepare-data --date 20260424 --model hrrr --forecast-hours 0-2 --products 2m_temperature_10m_winds,2m_dewpoint_10m_winds
rustwx render-maps --date 20260424 --model hrrr --domain california --product 2m_temperature_10m_winds --out-dir out
rustwx render-lightning --domain california --data-dir C:\Users\drew\lightning-test\data\glm --out-dir out
rustwx sample-point-timeseries --date 20260427 --cycle 0 --lat 40.802 --lon -124.164 --forecast-hour-end 6
rustwx-studio

rustwx-studio runs a no-AI browser UI directly from the rustwx wheel. The first surface includes every model/product advertised by agent_capabilities_json, GOES satellite rendering through render_goes_satellite_json, click-to-sounding, pressure VolumeStore cross-sections, WxStore export/import/plotting, point time-series sampling through sample_point_timeseries_json, and NEXRAD rendering when the optional radar_export binary is available on PATH or via --bin-dir. Studio launches generation through a local background job queue, so larger domain/product batches can be monitored from the browser while outputs stream into the configured artifact directory. The Prepare Data action and prepare_model_data_json API warm the shared GRIB cache for selected products, forecast hours, model runs, and sources before plotting, so subsequent map renders reuse cached subsets. The same workflow is available from the console as rustwx prepare-data. Interactive soundings and cross sections use a pressure VolumeStore when the optional store builder/renderers are available: the first request warms the model/domain/hour cache, and subsequent clicks render from the store rather than re-decoding pressure GRIBs.

render-maps accepts mixed product slugs and routes them to the appropriate direct, light derived, heavy ECAPE-derived, or HRRR windowed product path. Heavy ECAPE slugs such as sbecape, mlecape, muecape, ECAPE/CAPE ratios, NCAPE, ECIN, and ECAPE EHI/SCP/STP use the canonical derived_batch ECAPE path; they do not require callers to discover or run separate binaries.

When cache_dir / --cache-dir is omitted, the agent API uses a shared rustwx_outputs/cache fetch/decode cache, or RUSTWX_CACHE_DIR when that environment variable is set. The cache is intentionally independent of out_dir and map bounds, so city or bbox sweeps can reuse the same upstream GRIB fetches while writing PNGs into different output folders.

MCP servers should call these stable Python/CLI entry points instead of invoking internal proof binaries.

render_glm_lightning_json / rustwx render-lightning reads GOES GLM OR_GLM-L2-LCFA_*.nc files, renders native Rust projected flash maps, and writes a JSON flash artifact with lat/lon, time, energy, and area fields for agent consumption.

sample_point_timeseries_json samples native model fields at a lat/lon over a forecast-hour range for meteograms and point-and-click agent tools. The default variable set is HRRR-meteogram ready: 2 m T/Td/Tw/RH, 10 m wind/gust, hourly and accumulated QPF, low/mid/high clouds, MSLP, VPD, HDW, and the fire-weather composite. It uses rustwx's GRIB/idx fetch path and shared cache rather than cfgrib/xarray.

Every new projected helper has both a Python-object entry point and a _json variant:

• Python-object entry points accept either a JSON string or a JSON-serializable Python dict
• _json entry points keep returning pretty JSON strings for low-friction interop

Projected map API

The projected map surface is generic and public-facing. The caller supplies:

• lat, lon, field as numpy.ndarray 2-D arrays
• a render spec with product metadata, color scale, layout, and projection metadata
• optional contour, overlay, and wind layers

render_projected_map(...) writes the PNG and returns a Python dict with:

• typed projection, extents, layout, and layers sections
• legacy pixel_bounds, data_extent, valid_data_extent, and projection_info keys for compatibility

Minimal example

import rustwx

print(rustwx.list_models_json())

Point time-series example

import json
import rustwx

report = rustwx.sample_point_timeseries_json(json.dumps({
    "model": "hrrr",
    "date_yyyymmdd": "20260427",
    "cycle_utc": 0,
    "source": "nomads",
    "lat": 40.802,
    "lon": -124.164,
    "forecast_hour_start": 0,
    "forecast_hour_end": 6,
    "variables": ["temperature_2m_c", "relative_humidity_2m_pct", "wind_speed_10m_ms", "hdw"],
}))
print(report)

Projected render example

import rustwx

spec = {
    "output_path": "example.png",
    "product_key": "Example",
    "field_units": "dBZ",
    "scale": {
        "kind": "palette",
        "palette": "reflectivity",
        "levels": [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70],
        "extend": "Both",
    },
    "projection": {
        "map_proj": 1,
        "truelat1": 30.0,
        "truelat2": 60.0,
        "stand_lon": -97.0,
        "cen_lat": 38.0,
        "cen_lon": -97.0,
    },
    "width": 1100,
    "height": 850,
    "basemap_style": "none",
}

metadata = rustwx.render_projected_map(spec, lat, lon, field)
print(metadata["projection"]["kind"])
print(metadata["pixel_bounds"])

Geometry and overlay metadata example

surface = {
    "projection": spec["projection"],
    "width": 1100,
    "height": 850,
    "visual_mode": "filled_meteorology",
    "basemap_style": "filled",
}

geometry = rustwx.describe_projected_geometry(
    surface,
    lat,
    lon,
    include_projected_domain=False,
)
overlays = rustwx.build_projected_basemap_overlays(
    surface,
    lat,
    lon,
    include_geometry=False,
)

print(geometry["extents"]["padded"])
print(overlays["counts"])

Cross-section request normalization example

normalize_cross_section_request(...) does not render a cross-section yet. It validates and fills defaults for a future shared cross-section API surface.

xsect = rustwx.normalize_cross_section_request(
    {
        "path": {
            "start": {"lat": 39.74, "lon": -104.99, "label": "Denver"},
            "end": {"lat": 41.88, "lon": -87.63, "label": "Chicago"},
        },
        "field": {"product_key": "temperature", "field_units": "degC"},
    }
)

print(xsect["path_metrics"])
print(xsect["request"]["axis"])

Current limits

• projected rendering still expects caller-owned arrays
• cross-section support is validation/normalization only in this crate
• render_maps_json covers model fetch/download/render orchestration for direct, derived, heavy ECAPE-derived, and HRRR windowed map products
• sample_point_timeseries_json is a point data primitive; consumer-specific meteogram styling stays outside the Python wheel
• sounding rendering expects a caller-supplied validated column; model fetch and lat/lon extraction live in the Rust CLI for now