| Name | ncdata JSON |
| Version |
0.3.1
JSON |
| download |
| home_page | None |
| Summary | Abstract NetCDF data objects, providing fast data transfer between analysis packages. |
| upload_time | 2025-11-07 10:23:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.14,>=3.7 |
| license | BSD-3-Clause |
| keywords |
cf-metadata
data-analysis
netcdf
iris
xarray
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# ncdata
<img src="docs/_static/ncdata_logo_full.png" alt="ncdata logo" width="400" class="center">
Generic NetCDF data in Python.
Represents raw netCDF data in Python, with no structural assumptions or interpretations,
and provides facilities to inspect and manipulate it with complete freedom.
### Also
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](https://ncdata.readthedocs.io/en/stable/index.html)
* [latest](https://ncdata.readthedocs.io/en/latest/index.html)
# Demonstration code examples:
* [Apply Iris regrid to xarray data](#apply-iris-regrid-to-xarray-data)
* [Use Zarr data in Iris](#use-zarr-data-in-iris)
* [Correct a mis-coded attribute in Iris input](#correct-a-miscoded-attribute-in-iris-input)
* [Rename a dimension in xarray output](#rename-a-dimension-in-xarray-output)
* [Copy selected data to a new file](#copy-selected-data-to-a-new-file)
## Apply Iris regrid to xarray data
``` python
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
``` python
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
``` python
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
``` python
enable_lockshare(xarray=True)
dataset = xr.open_dataset("file1.nc")
xr_ncdata = from_xarray(dataset)
from ncdata.utils import rename_dimension
rename_dimension(xr_ncdata, "dim0", "newdim")
to_nc4(ncdata, "file_2a.nc")
```
## Copy selected data to a new file
``` python
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(var.name.startswith(key) for key in keys)
]
# Add any referenced coordinate variables
for var in select_vars:
coordnames = var.avals.get("coordinates")
if coordnames:
for coordname in coordnames.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")
```
Raw data
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"description": "# ncdata\n\n<img src=\"docs/_static/ncdata_logo_full.png\" alt=\"ncdata logo\" width=\"400\" class=\"center\">\n\nGeneric NetCDF data in Python.\n\nRepresents raw netCDF data in Python, with no structural assumptions or interpretations,\nand provides facilities to inspect and manipulate it with complete freedom.\n\n### Also\nProvides fast data exchange between analysis packages, and full control of storage\nformatting.\n\nEspecially : Ncdata **exchanges data between Xarray and Iris** as efficiently as possible \n> \"lossless, copy-free and lazy-preserving\".\n\nThis enables the user to freely mix+match operations from both projects, getting the\n\"best of both worlds\".\n > import xarray \n > import ncdata.iris_xarray as nci \n > import iris.quickplot as qplt \n > \n > ds = xarray.open_dataset(filepath) \n > ds_resample = ds.rolling(time=3).mean() \n > cubes = nci.cubes_from_xarray(ds_resample) \n > temp_cube = cubes.extract_cube(\"air_temperature\") \n > qplt.contourf(temp_cube[0])\n\n# Purposes\n * represent netcdf data as structures of Python objects\n * easy manipulation of netcdf data with pythonic syntax\n * Fast and efficient translation of data between Xarray and Iris objects.\n * This allows the user to mix+match features from either package in code. \n\nSee : https://ncdata.readthedocs.io/en/latest/userdocs/user_guide/design_principles.html\n\n# Documentation\nOn ReadTheDocs. Please see: \n * [stable](https://ncdata.readthedocs.io/en/stable/index.html)\n * [latest](https://ncdata.readthedocs.io/en/latest/index.html)\n\n# Demonstration code examples:\n * [Apply Iris regrid to xarray data](#apply-iris-regrid-to-xarray-data)\n * [Use Zarr data in Iris](#use-zarr-data-in-iris)\n * [Correct a mis-coded attribute in Iris input](#correct-a-miscoded-attribute-in-iris-input)\n * [Rename a dimension in xarray output](#rename-a-dimension-in-xarray-output)\n * [Copy selected data to a new file](#copy-selected-data-to-a-new-file)\n\n## Apply Iris regrid to xarray data\n``` python\nfrom ncdata.iris_xarray import cubes_to_xarray, cubes_from_xarray\ndataset = xarray.open_dataset(\"file1.nc\", chunks=\"auto\")\n(cube,) = cubes_from_xarray(dataset)\ncube2 = cube.regrid(grid_cube, iris.analysis.PointInCell)\ndataset2 = cubes_to_xarray(cube2)\n```\n\n## Use Zarr data in Iris\n``` python\nfrom ncdata.threadlock_sharing import enable_lockshare\nenable_lockshare(iris=True, xarray=True)\nimport xarray as xr\ndataset = xr.open_dataset(input_zarr_path, engine=\"zarr\", chunks=\"auto\")\ninput_cubes = cubes_from_xarray(dataset)\noutput_cubes = my_process(input_cubes)\ndataset2 = cubes_to_xarray(output_cubes)\ndataset2.to_zarr(output_zarr_path)\n``` \n\n## Correct a miscoded attribute in Iris input\n``` python\nfrom ncdata.iris import to_iris\nenable_lockshare(iris=True)\nncdata = from_nc4(input_path)\nfor var in ncdata.variables.values():\n if \"coords\" in var.attributes:\n var.attributes.rename(\"coords\", \"coordinates\")\ncubes = to_iris(ncdata)\n```\n\n## Rename a dimension in xarray output\n``` python\nenable_lockshare(xarray=True)\ndataset = xr.open_dataset(\"file1.nc\")\nxr_ncdata = from_xarray(dataset)\nfrom ncdata.utils import rename_dimension\nrename_dimension(xr_ncdata, \"dim0\", \"newdim\")\nto_nc4(ncdata, \"file_2a.nc\")\n```\n\n## Copy selected data to a new file\n``` python\nfrom ncdata.netcdf4 import from_nc4, to_nc4\nncdata = from_nc4(\"file1.nc\")\n\n# Make a list of partial names to select the wanted variables\nkeys = [\"air_\", \"surface\"]\n\n# Explicitly add dimension names, to include all the dimension variables\nkeys += list(ncdata.dimensions)\n\n# Identify the wanted variables\nselect_vars = [\n var\n for var in ncdata.variables.values()\n if any(var.name.startswith(key) for key in keys)\n]\n\n# Add any referenced coordinate variables\nfor var in select_vars:\n coordnames = var.avals.get(\"coordinates\")\n if coordnames:\n for coordname in coordnames.split(\" \"):\n select_vars.append(ncdata.variables[coordname])\n\n# Replace variables with only the wanted ones\nncdata.variables.clear()\nncdata.variables.addall(select_vars)\n\n# Save\nto_nc4(ncdata, \"pruned.nc\")\n```\n",
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