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The NetCDF-flattener package

Project description


Flatten netCDF files while preserving references as described in the CF Conventions 1.8, chapter 2.7.


The flattener takes as input and output NetCDF Dataset objects, which the user can create or open from ".nc" files using the netCDF4 API. To flatten the Dataset named "input_dataset" into a Dataset named "output_dataset", use the following command. In most cases, "output_dataset" will be an empty Dataset.

import netcdf_flattener
netcdf_flattener.flatten(input_dataset, output_dataset)

By default, the flattener is in strict mode and returns an exception if a an internal reference from a variable attribute to a dimension or variable could not be resolved. To use the lax mode that continues the flattening process with warning, specify the lax_mode parameter:

netcdf_flattener.flatten(input_dataset, output_dataset, lax_mode=True)

For copying variables that would otherwise be larger than the available memory, the copy_slices parameter allows to specify slices to be used when copying the variable. They are specified per variable in a dictionary. The slicing shape is either None for using a default slice value, or a custom slicing shape in the form of a tuple of the same dimension as the variable. If a variable from the Dataset is not contained in the dict, it will not be sliced and copied normally.

Slice shapes should be small enough to fit in memory, but not too small larges loops on small slice can degrade performances drastically. Typically, slices of size in the order of 10^6 to 10^8 are suitable.

netcdf_flattener.flatten(input_dataset, output_dataset, copy_slices={"/grp1/var1": (1000,1000,500,), "/grp1/var3": None})


When a CF coordinate variable in the input dataset is in a different group to its corresponding dimension, the same variable in the output flattened dataset will no longer be a CF coordinate variable, as its name will be prefixed with a different group identifier than its dimension. In such cases, it is up to the user to apply the proximal and lateral search alogrithms, in conjunction with the mappings defined in the flattener_name_mapping_variables and flattener_name_mapping_dimensions global attributes, to find which netCDF variables are acting as CF coordinate variables in the flattened dataset.

For example, if an input dataset has dimension lat in the root group and coordinate variable lat(lat) in group /grp1, then the flattened dataset will contain dimension lat and variable grp1__lat(lat), both in its root group. In this case, the flattener_name_mapping_variables global attribute of the flattened dataset will contain the mapping "grp1__lat: /grp1/lat" and the flattener_name_mapping_dimensions global attribute will contain the mapping "lat: /lat".


From PyPi

netCDF-flattener is in installable with pip, for example:

pip install netcdf-flattener

From source

Install the build dependencies:

python3 -m pip install --upgrade pip setuptools wheel

Download the source code from and compile the wheel file, by running the following command from the repository root:

python3 bdist_wheel

Install the wheel file using pip:

python3 -m pip install dist/netcdf_flattener-*.whl


Questions and issues should be raised at the issue tracker in the canonical source code repository:

Automated testing


Running the tests requires having the NetCDF4 libraries installed (ncdump and ncgen applications are required). You can install them either using your package manager, or build them from the source.

On CentOS: sudo yum install netcdf

Install Pytest:

python3 -m pip install pytest

All other dependencies are managed by pip and use OSI-approved licenses.

Run the tests

Run Pytest from the root of the repository:

python3 -m pytest


A Sphinx project is provided to generate the HTML documentation from the code.

Install Sphinx:

python3 -m pip install sphinx

From the "doc" folder, build the documentation:

cd doc
sphinx-build -b html . build

The entry point to the documentation is the doc/build/index.html file.


This code is under Apache 2.0 License. See LICENSE for the full license text.


See AUTHORS for details.

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