ncdata

Generic NetCDF data in Python.

Provides fast data exchange between analysis packages, and full control of storage formatting.

Especially : Ncdata exchanges data between Xarray and Iris as efficiently as possible

"lossless, copy-free and lazy-preserving".

This enables the user to freely mix+match operations from both projects, getting the "best of both worlds".

import xarray
import ncdata.iris_xarray as nci
import iris.quickplot as qplt

ds = xarray.open_dataset(filepath)
ds_resample = ds.rolling(time=3).mean()
cubes = nci.cubes_from_xarray(ds_resample)
temp_cube = cubes.extract_cube("air_temperature")
qplt.contourf(temp_cube[0])

Purposes

• represent netcdf data as structures of Python objects
• easy manipulation of netcdf data with pythonic syntax
• Fast and efficient translation of data between Xarray and Iris objects.
  • This allows the user to mix+match features from either package in code.

See : https://ncdata.readthedocs.io/en/latest/userdocs/user_guide/design_principles.html

Documentation

On ReadTheDocs. Please see:

• stable
• latest

Demonstration code examples:

• Apply Iris regrid to xarray data
• Use Zarr data in Iris
• Correct a mis-coded attribute in Iris input
• Rename a dimension in xarray output
• Copy selected data to a new file

Apply Iris regrid to xarray data

from ncdata.iris_xarray import cubes_to_xarray, cubes_from_xarray
dataset = xarray.open_dataset("file1.nc", chunks="auto")
(cube,) = cubes_from_xarray(dataset)
cube2 = cube.regrid(grid_cube, iris.analysis.PointInCell)
dataset2 = cubes_to_xarray(cube2)

Use Zarr data in Iris

from ncdata.threadlock_sharing import enable_lockshare
enable_lockshare(iris=True, xarray=True)
import xarray as xr
dataset = xr.open_dataset(input_zarr_path, engine="zarr", chunks="auto")
input_cubes = cubes_from_xarray(dataset)
output_cubes = my_process(input_cubes)
dataset2 = cubes_to_xarray(output_cubes)
dataset2.to_zarr(output_zarr_path)

Correct a miscoded attribute in Iris input

from ncdata.iris import to_iris
enable_lockshare(iris=True)
ncdata = from_nc4(input_path)
for var in ncdata.variables.values():
    if "coords" in var.attributes:
      var.attributes.rename("coords", "coordinates")
cubes = to_iris(ncdata)

Rename a dimension in xarray output

enable_lockshare(xarray=True)
dataset = xr.open_dataset("file1.nc")
xr_ncdata = from_xarray(dataset)
xr_ncdata.dimensions.rename("dim0", "newdim")
# N.B. must also replace the name in dimension-lists of variables
for var in xr_ncdata.variables.values():
    var.dimensions = ["newdim" if dim == "dim0" else dim for dim in var.dimensions]
to_nc4(ncdata, "file_2a.nc")

Copy selected data to a new file

from ncdata.netcdf4 import from_nc4, to_nc4
ncdata = from_nc4("file1.nc")

# Make a list of partial names to select the wanted variables
keys = ["air_", "surface"]

# Explicitly add dimension names, to include all the dimension variables
keys += + list(ncdata.dimensions)

# Identify the wanted variables
select_vars = [
    var
    for var in ncdata.variables.values()
    if any(key in var.name for key in keys)
]

# Add any referenced coordinate variables
for var in list(select_vars):
    var = ncdata.variables[varname]
    for coordname in var.attributes.get("coordinates", "").split(" "):
        select_vars.append(ncdata.variables[coordname])

# Replace variables with only the wanted ones
ncdata.variables.clear()
ncdata.variables.addall(select_vars)

# Save
to_nc4(ncdata, "pruned.nc")

Older References in Iris

• Iris issue : SciTools/iris#4994
• planning presentation : https://github.com/SciTools/iris/files/10499677/Xarray-Iris.bridge.proposal.--.NcData.pdf
• in-Iris code workings : pp-mo/iris#75