nc-check 0.2.0

Prepare xarray Datasets for CF-1.12 compliant NetCDF output

nc-check

Prepare xarray.Dataset objects to be written as CF-1.12-compliant NetCDF files.

Install

uv sync

Install optional CF checker dependencies (recommended for full CF checks):

uv sync --extra cf

Usage

import xarray as xr
import nc_check  # Registers the .check dataset accessor

ds = xr.Dataset(
    data_vars={"temp": (("time", "lat", "lon"), [[[280.0]]])},
    coords={"time": [0], "lat": [10.0], "lon": [20.0]},
)

issues = ds.check.compliance(report_format="python")
fixed = ds.check.make_cf_compliant()
ocean_report = ds.check.ocean_cover(report_format="python")
time_report = ds.check.time_cover(report_format="python")

By default (report_format="auto"), reports adapt to environment:

• Jupyter notebooks: HTML
• CLI terminals: rich tables
• Other environments (scripts/tests): Python dicts

You can request rich table reports printed to stdout:

ds.check.compliance(report_format="tables")

CLI usage:

# Prints readable rich tables to stdout (default behavior)
nc-check input.nc

# Explicit check modes
nc-check compliance input.nc
nc-check compliance input.nc --engine heuristic
nc-check ocean-cover input.nc
nc-check time-cover input.nc
nc-check all input.nc
nc-check all input.nc --engine cfchecker

# Explicit coordinate names when they differ from lon/lat/time
nc-check ocean-cover input.nc --lon-name x --lat-name y --time-name t
nc-check time-cover input.nc --time-name t
nc-check all input.nc --lon-name x --lat-name y --time-name t

# Saves an HTML report beside input.nc as input_report.html
nc-check input.nc --save-report

# With "all", saves one combined report:
# input_all_report.html
nc-check all input.nc --save-report

For notebooks, use HTML output and optionally save it:

html = ds.check.compliance(
    report_format="html",
    report_html_file="cf-report.html",
)

You can choose which conventions to check:

ds.check.compliance(conventions="cf,ferret")
ds.check.compliance(conventions="ferret")  # custom-only checks
ds.check.compliance(engine="heuristic")  # force built-in heuristic engine
ds.check.compliance(engine="cfchecker")  # require cfchecker path

# Explicit coordinate names
ds.check.ocean_cover(lon_name="x", lat_name="y", time_name="t")
ds.check.time_cover(time_name="t")
ds.check.all(lon_name="x", lat_name="y", time_name="t")

compliance() runs cf-checker against an in-memory NetCDF payload created from dataset metadata (no .nc file written to disk). With report_format="python" it returns a dictionary of detected issues.

make_cf_compliant() returns a new dataset with safe automatic fixes, including:

• Conventions = "CF-1.12"
• standard coordinate attributes for inferred time, lat, and lon axes
• creation of missing dimension coordinates for inferred axes
• global extent attributes derived from inferred axes: time_coverage_start, time_coverage_end, geospatial_lat_min, geospatial_lat_max, geospatial_lon_min, and geospatial_lon_max

ocean_cover() runs fast ocean-grid checks and returns a report with:

• east/west edge-of-map detection (persistent missing longitude columns, reported by longitude),
• land/ocean sanity checks at fixed reference points (offset detection).
• report_format="auto" by default.
• When var_name is omitted, all data variables with inferred lat/lon dimensions are checked.

time_cover() runs time-dimension missing-data checks and reports missing time-slice ranges.

• report_format="auto" by default.
• When var_name is omitted, all data variables are checked.
• Time coordinate units/type validation is reported by compliance().

You can disable individual checks:

ds.check.ocean_cover(
    check_edge_of_map=True,
    check_land_ocean_offset=True,
    report_format="python",
)

ds.check.time_cover(
    report_format="python",
)

Notes:

• cfchecker requires the system udunits2 library via cfunits.
• For large files, prefer opening with lazy chunks: xr.open_dataset(path, chunks={}).
• The built-in ferret convention flags coordinate _FillValue usage as an error.
• If cfchecker cannot run, compliance() falls back to heuristic checks and includes a checker_error field in the response.
• You can choose the compliance engine explicitly with engine="heuristic" or engine="cfchecker" (CLI: --engine).
• The report engine field reflects what ran: cfchecker, heuristic, or none (when CF checks are skipped, e.g. conventions="ferret").
• You can bias standard-name suggestions by domain, e.g. ds.check.compliance(domain="ocean") (also supports atmosphere, land, cryosphere, and biogeochemistry).